meaning speech recognition

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

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What is Speech Recognition?

Speech recognition or speech-to-text recognition, is the capacity of a machine or program to recognize spoken words and transform them into text. Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. In this article, we are going to discuss every point about speech recognition.

Speech Recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, focuses on enabling computers to understand and interpret human speech. Speech recognition involves converting spoken language into text or executing commands based on the recognized words. This technology relies on sophisticated algorithms and machine learning models to process and understand human speech in real-time, despite the variations in accents, pitch, speed, and slang.

Features of Speech Recognition

• Accuracy and Speed: They can process speech in real-time or near real-time, providing quick responses to user inputs.
• Natural Language Understanding (NLU): NLU enables systems to handle complex commands and queries, making technology more intuitive and user-friendly .
• Multi-Language Support: Support for multiple languages and dialects, allowing users from different linguistic backgrounds to interact with technology in their native language.
• Background Noise Handling: This feature is crucial for voice-activated systems used in public or outdoor settings.

Speech Recognition Algorithms

Speech recognition technology relies on complex algorithms to translate spoken language into text or commands that computers can understand and act upon. Here are the algorithms and approaches used in speech recognition:

1. Hidden Markov Models (HMM)

Hidden Markov Models have been the backbone of speech recognition for many years. They model speech as a sequence of states, with each state representing a phoneme (basic unit of sound) or group of phonemes. HMMs are used to estimate the probability of a given sequence of sounds, making it possible to determine the most likely words spoken. Usage : Although newer methods have surpassed HMM in performance, it remains a fundamental concept in speech recognition, often used in combination with other techniques.

2. Natural language processing (NLP)

NLP is the area of  artificial intelligence  which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search. Example such as: Siri or provide more accessibility around texting. 

3. Deep Neural Networks (DNN)

DNNs have improved speech recognition’s accuracy a lot. These networks can learn hierarchical representations of data, making them particularly effective at modeling complex patterns like those found in human speech. DNNs are used both for acoustic modeling, to better understand the sound of speech, and for language modeling, to predict the likelihood of certain word sequences.

4. End-to-End Deep Learning

Now, the trend has shifted towards end-to-end deep learning models, which can directly map speech inputs to text outputs without the need for intermediate phonetic representations. These models, often based on advanced RNNs, Transformers, or Attention Mechanisms, can learn more complex patterns and dependencies in the speech signal.

How does Speech Recognition Work?

Speech recognition systems works on computer algorithms to process and interpret spoken words before converting them into text. A software program converts the sound into written text that computers and humans can understand by analyzing the audio, broke down into segments, digitize into readable format and apply most suitable algorithm. Human speech is very diverse and context-specific, thus speech recognition software has to adapt accordingly. The software algorithms that interpret and organise audio into text are trained on a variety of speech patterns, speaking styles, languages, dialects, accents, and phrasing. The software also distinguishes spoken audio from noise from the background. Speech recognition uses two types model:

• Acoustic Model: An acoustic model is responsible for converting an audio signal into a sequence of phonemes or sub-word units. It represents the relationship between acoustic signals and phonemes or sub-word units.
• Language Model: A language model is responsible for assigning probabilities to sequences of words or phrases. It captures the likelihood of certain word sequences occurring in a given language. Language models can be based on n-gram models, recurrent neural networks (RNNs) , or transformer-based architectures like GPT (Generative Pre-trained Transformer).

Speech Recognition Use Cases

• Virtual Assistants : These assistants use speech recognition to understand user commands and questions, enabling hands-free interaction for tasks like setting reminders, searching the internet, controlling smart home devices, and more. For Ex – Siri, Alexa, Google Assistant
• Accessibility Tools : Speech recognition improves accessibility, allowing individuals with physical disabilities to interact with technology and communicate more easily. For Ex – Voice control features in smartphones and computers, specialized applications for individuals with disabilities.
• Automotive Systems : Drivers can use voice commands to control navigation systems, music, and phone calls, reducing distractions and enhancing safety on the road. For Ex – Voice-activated navigation and infotainment systems in cars.
• Healthcare : Doctors and medical staff use speech recognition for faster documentation, allowing them to spend more time with patients. Additionally, voice-enabled bots can assist in patient care and inquiries. For Ex – Dictation solutions for medical documentation, patient interaction bots.
• Customer Service : Speech recognition is used to route customer calls to the appropriate department or to provide automated assistance, improving efficiency and customer satisfaction. For Ex – Voice-operated call centers, customer service bots.
• Education and E-Learning : Speech recognition aids in language learning by providing immediate feedback on pronunciation. It also helps in transcribing lectures and seminars for better accessibility. For Ex – Language learning apps, lecture transcription services.
• Security and Authentication : Speech recognition combined with voice biometrics offers a secure and convenient way to authenticate users for banking services, secure facilities, and personal devices. For Ex – Voice biometrics in banking and secure access.
• Entertainment and Media : Users can find content using voice search, making navigation easier and more intuitive. Voice-controlled games offer a unique, hands-free gaming experience. For Ex – Voice biometrics in banking and secure access.

Speech Recognition Vs Voice Recognition

Speech Recognition is better for applications where the goal is to understand and convert spoken language into text or commands . This makes it ideal for creating hands-free user interfaces, transcribing meetings or lectures, enabling voice commands for devices, and assisting users with disabilities. Whereas Voice Recognition is better for applications focused on identifying or verifying the identity of a speaker. This technology is crucial for security and personalized interaction, such as biometric authentication, personalized user experiences based on the identified speaker, and access control systems. Its value comes from its ability to recognize the unique characteristics of a person’s voice, offering a layer of security or customization.

Advantages of Speech Recognition

• Accessibility: Speech recognition technology improves accessibility for individuals with disabilities, including those with mobility impairments or vision loss.
• Increased Productivity: Speech recognition can significantly enhance productivity by enabling faster data entry and document creation.
• Hands-Free Operation:  Enables hands-free interaction with devices and systems, improving safety and convenience, especially in tasks like driving or cooking.
• Efficiency:  Speeds up data entry and interaction with devices, as speaking is often faster than typing or using a keyboard.
• Multimodal Interaction:  Supports multimodal interfaces, allowing users to combine speech with other input methods like touch and gestures for more natural interactions.

Disadvantages of Speech Recognition

• Inconsistent performance: The systems may be unable to record words accurately due to variations in pronunciation, a lack of capability for particular languages, and the inability to sift through background noise.
• Speed: Some voice recognition programs require time to implement and learn. Speech processing is relatively slow.
• Source file issues: Speech recognition is dependent on the recording equipment utilised, not simply the programme.
• Dependence on Infrastructure: Effective speech recognition frequently relies on strong infrastructure, such as consistent internet connectivity and computing resources.

Speech recognition is a powerful technology that lets computers understand and process human speech. It’s used everywhere, from asking your smartphone for directions to controlling your smart home devices with just your voice. This tech makes life easier by helping with tasks without needing to type or press buttons, making gadgets like virtual assistants more helpful. It’s also super important for making tech accessible to everyone, including those who might have a hard time using keyboards or screens. As we keep finding new ways to use speech recognition, it’s becoming a big part of our daily tech life, showing just how much we can do when we talk to our devices.

Frequently Asked Question on Speech Recognition – FAQs

What are examples of speech recognition.

Note Taking/Writing: An example of speech recognition technology in use is speech-to-text platforms such as Speechmatics or Google’s speech-to-text engine. In addition, many voice assistants offer speech-to-text translation.

Is speech recognition secure?

Security concerns related to speech recognition primarily involve the privacy and protection of audio data collected and processed by speech recognition systems. Ensuring secure data transmission, storage, and processing is essential to address these concerns.

What is speech recognition in AI?

Speech recognition is the process of converting sound signals to text transcriptions. Steps involved in conversion of a sound wave to text transcription in a speech recognition system are: Recording: Audio is recorded using a voice recorder. Sampling: Continuous audio wave is converted to discrete values.

How accurate is speech recognition technology?

The accuracy of speech recognition technology can vary depending on factors such as the quality of audio input, language complexity, and the specific application or system being used. Advances in machine learning and deep learning have improved accuracy significantly in recent years.

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Speech Recognition: Everything You Need to Know in 2024

Speech recognition, also known as automatic speech recognition (ASR) , enables seamless communication between humans and machines. This technology empowers organizations to transform human speech into written text. Speech recognition technology can revolutionize many business applications , including customer service, healthcare, finance and sales.

In this comprehensive guide, we will explain speech recognition, exploring how it works, the algorithms involved, and the use cases of various industries.

If you require training data for your speech recognition system, here is a guide to finding the right speech data collection services.

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text.

Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents, dialects, and speech patterns.

What are the features of speech recognition systems?

Speech recognition systems have several components that work together to understand and process human speech. Key features of effective speech recognition are:

• Audio preprocessing: After you have obtained the raw audio signal from an input device, you need to preprocess it to improve the quality of the speech input The main goal of audio preprocessing is to capture relevant speech data by removing any unwanted artifacts and reducing noise.
• Feature extraction: This stage converts the preprocessed audio signal into a more informative representation. This makes raw audio data more manageable for machine learning models in speech recognition systems.
• Language model weighting: Language weighting gives more weight to certain words and phrases, such as product references, in audio and voice signals. This makes those keywords more likely to be recognized in a subsequent speech by speech recognition systems.
• Acoustic modeling : It enables speech recognizers to capture and distinguish phonetic units within a speech signal. Acoustic models are trained on large datasets containing speech samples from a diverse set of speakers with different accents, speaking styles, and backgrounds.
• Speaker labeling: It enables speech recognition applications to determine the identities of multiple speakers in an audio recording. It assigns unique labels to each speaker in an audio recording, allowing the identification of which speaker was speaking at any given time.
• Profanity filtering: The process of removing offensive, inappropriate, or explicit words or phrases from audio data.

What are the different speech recognition algorithms?

Speech recognition uses various algorithms and computation techniques to convert spoken language into written language. The following are some of the most commonly used speech recognition methods:

• Hidden Markov Models (HMMs): Hidden Markov model is a statistical Markov model commonly used in traditional speech recognition systems. HMMs capture the relationship between the acoustic features and model the temporal dynamics of speech signals.
• Estimate the probability of word sequences in the recognized text
• Convert colloquial expressions and abbreviations in a spoken language into a standard written form
• Map phonetic units obtained from acoustic models to their corresponding words in the target language.
• Speaker Diarization (SD): Speaker diarization, or speaker labeling, is the process of identifying and attributing speech segments to their respective speakers (Figure 1). It allows for speaker-specific voice recognition and the identification of individuals in a conversation.

Figure 1: A flowchart illustrating the speaker diarization process

• Dynamic Time Warping (DTW): Speech recognition algorithms use Dynamic Time Warping (DTW) algorithm to find an optimal alignment between two sequences (Figure 2).

Figure 2: A speech recognizer using dynamic time warping to determine the optimal distance between elements

5. Deep neural networks: Neural networks process and transform input data by simulating the non-linear frequency perception of the human auditory system.

6. Connectionist Temporal Classification (CTC): It is a training objective introduced by Alex Graves in 2006. CTC is especially useful for sequence labeling tasks and end-to-end speech recognition systems. It allows the neural network to discover the relationship between input frames and align input frames with output labels.

Speech recognition vs voice recognition

Speech recognition is commonly confused with voice recognition, yet, they refer to distinct concepts. Speech recognition converts  spoken words into written text, focusing on identifying the words and sentences spoken by a user, regardless of the speaker’s identity. 

On the other hand, voice recognition is concerned with recognizing or verifying a speaker’s voice, aiming to determine the identity of an unknown speaker rather than focusing on understanding the content of the speech.

What are the challenges of speech recognition with solutions?

While speech recognition technology offers many benefits, it still faces a number of challenges that need to be addressed. Some of the main limitations of speech recognition include:

Acoustic Challenges:

• Assume a speech recognition model has been primarily trained on American English accents. If a speaker with a strong Scottish accent uses the system, they may encounter difficulties due to pronunciation differences. For example, the word “water” is pronounced differently in both accents. If the system is not familiar with this pronunciation, it may struggle to recognize the word “water.”

Solution: Addressing these challenges is crucial to enhancing  speech recognition applications’ accuracy. To overcome pronunciation variations, it is essential to expand the training data to include samples from speakers with diverse accents. This approach helps the system recognize and understand a broader range of speech patterns.

• For instance, you can use data augmentation techniques to reduce the impact of noise on audio data. Data augmentation helps train speech recognition models with noisy data to improve model accuracy in real-world environments.

Figure 3: Examples of a target sentence (“The clown had a funny face”) in the background noise of babble, car and rain.

Linguistic Challenges:

• Out-of-vocabulary words: Since the speech recognizers model has not been trained on OOV words, they may incorrectly recognize them as different or fail to transcribe them when encountering them.

Figure 4: An example of detecting OOV word

Solution: Word Error Rate (WER) is a common metric that is used to measure the accuracy of a speech recognition or machine translation system. The word error rate can be computed as:

Figure 5: Demonstrating how to calculate word error rate (WER)

• Homophones: Homophones are words that are pronounced identically but have different meanings, such as “to,” “too,” and “two”. Solution: Semantic analysis allows speech recognition programs to select the appropriate homophone based on its intended meaning in a given context. Addressing homophones improves the ability of the speech recognition process to understand and transcribe spoken words accurately.

Technical/System Challenges:

• Data privacy and security: Speech recognition systems involve processing and storing sensitive and personal information, such as financial information. An unauthorized party could use the captured information, leading to privacy breaches.

Solution: You can encrypt sensitive and personal audio information transmitted between the user’s device and the speech recognition software. Another technique for addressing data privacy and security in speech recognition systems is data masking. Data masking algorithms mask and replace sensitive speech data with structurally identical but acoustically different data.

Figure 6: An example of how data masking works

• Limited training data: Limited training data directly impacts  the performance of speech recognition software. With insufficient training data, the speech recognition model may struggle to generalize different accents or recognize less common words.

Solution: To improve the quality and quantity of training data, you can expand the existing dataset using data augmentation and synthetic data generation technologies.

13 speech recognition use cases and applications

In this section, we will explain how speech recognition revolutionizes the communication landscape across industries and changes the way businesses interact with machines.

Customer Service and Support

• Interactive Voice Response (IVR) systems: Interactive voice response (IVR) is a technology that automates the process of routing callers to the appropriate department. It understands customer queries and routes calls to the relevant departments. This reduces the call volume for contact centers and minimizes wait times. IVR systems address simple customer questions without human intervention by employing pre-recorded messages or text-to-speech technology . Automatic Speech Recognition (ASR) allows IVR systems to comprehend and respond to customer inquiries and complaints in real time.
• Customer support automation and chatbots: According to a survey, 78% of consumers interacted with a chatbot in 2022, but 80% of respondents said using chatbots increased their frustration level.
• Sentiment analysis and call monitoring: Speech recognition technology converts spoken content from a call into text. After  speech-to-text processing, natural language processing (NLP) techniques analyze the text and assign a sentiment score to the conversation, such as positive, negative, or neutral. By integrating speech recognition with sentiment analysis, organizations can address issues early on and gain valuable insights into customer preferences.
• Multilingual support: Speech recognition software can be trained in various languages to recognize and transcribe the language spoken by a user accurately. By integrating speech recognition technology into chatbots and Interactive Voice Response (IVR) systems, organizations can overcome language barriers and reach a global audience (Figure 7). Multilingual chatbots and IVR automatically detect the language spoken by a user and switch to the appropriate language model.

Figure 7: Showing how a multilingual chatbot recognizes words in another language

• Customer authentication with voice biometrics: Voice biometrics use speech recognition technologies to analyze a speaker’s voice and extract features such as accent and speed to verify their identity.

Sales and Marketing:

• Virtual sales assistants: Virtual sales assistants are AI-powered chatbots that assist customers with purchasing and communicate with them through voice interactions. Speech recognition allows virtual sales assistants to understand the intent behind spoken language and tailor their responses based on customer preferences.
• Transcription services : Speech recognition software records audio from sales calls and meetings and then converts the spoken words into written text using speech-to-text algorithms.

Automotive:

• Voice-activated controls: Voice-activated controls allow users to interact with devices and applications using voice commands. Drivers can operate features like climate control, phone calls, or navigation systems.
• Voice-assisted navigation: Voice-assisted navigation provides real-time voice-guided directions by utilizing the driver’s voice input for the destination. Drivers can request real-time traffic updates or search for nearby points of interest using voice commands without physical controls.

Healthcare:

• Recording the physician’s dictation
• Transcribing the audio recording into written text using speech recognition technology
• Editing the transcribed text for better accuracy and correcting errors as needed
• Formatting the document in accordance with legal and medical requirements.
• Virtual medical assistants: Virtual medical assistants (VMAs) use speech recognition, natural language processing, and machine learning algorithms to communicate with patients through voice or text. Speech recognition software allows VMAs to respond to voice commands, retrieve information from electronic health records (EHRs) and automate the medical transcription process.
• Electronic Health Records (EHR) integration: Healthcare professionals can use voice commands to navigate the EHR system , access patient data, and enter data into specific fields.

Technology:

• Virtual agents: Virtual agents utilize natural language processing (NLP) and speech recognition technologies to understand spoken language and convert it into text. Speech recognition enables virtual agents to process spoken language in real-time and respond promptly and accurately to user voice commands.

Further reading

• Top 5 Speech Recognition Data Collection Methods in 2023
• Top 11 Speech Recognition Applications in 2023

External Links

• 1. Databricks
• 2. PubMed Central
• 3. Qin, L. (2013). Learning Out-of-vocabulary Words in Automatic Speech Recognition . Carnegie Mellon University.
• 4. Wikipedia

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Top 11 Voice Recognition Applications in 2024

What Is Speech Recognition?

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The human voice allows people to express their thoughts, emotions, and ideas through sound. Speech separates us from computing technology, but both similarly rely on words to transform ideas into shared understanding. In the past, we interfaced with computers and applications only through keyboards, controllers, and consoles—all hardware. But today, speech recognition software bridges the gap that separates speech and text.

First, let’s start with the meaning of automatic speech recognition: it’s the process of converting what speakers say into written or electronic text. Potential business applications include everything from customer support to translation services.

Now that you understand what speech recognition is, read on to learn how speech recognition works, different speech recognition types, and how your business can benefit from speech recognition applications.

How does speech recognition work?

Speech recognition technologies capture the human voice with physical devices like receivers or microphones. The hardware digitizes recorded sound vibrations into electrical signals. Then, the software attempts to identify sounds and phonemes—the smallest unit of speech—from the signals and match these sounds to corresponding text. Depending on the application, this text displays on the screen or triggers a directive—like when you ask your smart speaker to play a specific song and it does.

Background noise, accents, slang, and cross talk can interfere with speech recognition, but advancements in artificial intelligence (AI) and machine learning technologies filter through these anomalies to increase precision and performance.

Thanks to new and emerging machine learning algorithms, speech recognition offers advanced capabilities:

• Natural language processing is a branch of computer science that uses AI to emulate how humans engage in and understand speech and text-based interactions.
• Hidden Markov Models (HMM) are statistical models that assign text labels to units of speech—like words, syllables, and sentences—in a sequence. Labels map to the provided input to determine the correct label or text sequence.
• N-grams are language models that assign probabilities to sentences or phrases to improve speech recognition accuracy. These contain sequences of words and use prior sequences of the same words to understand or predict new words and phrases. These calculations improve the predictions of sentence automatic completion systems, spell-check results, and even grammar checks.
• Neural networks consist of node layers that together emulate the learning and decision-making capabilities of the human brain. Nodes contain inputs, weights, a threshold, and an output value. Outputs that exceed the threshold activate the corresponding node and pass data to the next layer. This means remembering earlier words to continually improve recognition accuracy.
• Connectionist temporal classification is a neural network algorithm that uses probability to map text transcript labels to incoming audio. It helps train neural networks to understand speech and build out node networks.

Features of speech recognition

Not all speech recognition works the same. Implementations vary by application, but each uses AI to quickly process speech at a high—but not flawless—quality level. Many speech recognition technologies include the same features:

• Filtering identifies and censors—or removes—specified words or phrases to sanitize text outputs.
• Language weighting assigns more value to frequently spoken words—like proper nouns or industry jargon—to improve speech recognition precision.
• Speaker labeling distinguishes between multiple conversing speakers by identifying contributions based on vocal characteristics.
• Acoustics training analyzes conditions—like ambient noise and particular speaker styles—then tailors the speech recognition software to that environment. It’s useful when recording speech in busy locations, like call centers and offices.
• Voice recognition helps speech recognition software pivot the listening approach to each user’s accent, dialect, and grammatical library.

5 benefits of speech recognition technology

The popularity and convenience of speech recognition technology have made speech recognition a big part of everyday life. Adoption of this technology will only continue to spread, so learn more about how speech recognition transforms how we live and work:

• Speed: Speaking with your voice is faster than typing with your fingers—in most cases.
• Assistance: Listening to directions from users and taking action accordingly is possible thanks to speech recognition technology. For instance, if your vehicle’s sound system has speech recognition capabilities, you can tell it to tune the radio to a particular channel or map directions to a specified address.
• Productivity: Dictating your thoughts and ideas instead of typing them out, saves time and effort to redirect toward other tasks. To illustrate, picture yourself dictating a report into your smartphone while walking or driving to your next meeting.
• Intelligence: Learning from and adapting to your unique speech habits and environment to identify and understand you better over time is possible thanks to speech recognition applications.
• Accessibility: Entering text with speech recognition is possible for people with visual impairments who can’t see a keyboard thanks to this technology. Software and websites like Google Meet and YouTube can accommodate hearing-impaired viewers with text captions of live speech translated to the user’s specific language.

Business speech recognition use cases

Speech recognition directly connects products and services to customers. It powers interactive voice recognition software that delivers customers to the right support agents—each more productive with faster, hands-free communication. Along the way, speech recognition captures actionable insights from customer conversations you can use to bolster your organization’s operational and marketing processes.

Here are some real-world speech recognition contexts and applications:

• SMS/MMS messages: Write and send SMS or MMS messages conveniently in some environments.
• Chatbot discussions: Get answers to product or service-related questions any time of day or night with chatbots.
• Web browsing : Browse the internet without a mouse, keyboard, or touch screen through voice commands.
• Active learning: Enable students to enjoy interactive learning applications—such as those that teach a new language—while teachers create lesson plans.
• Document writing: Draft a Google or Word document when you can't access a physical or digital keyboard with speech-to-text. You can later return to the document and refine it once you have an opportunity to use a keyboard. Doctors and nurses often use these applications to log patient diagnoses and treatment notes efficiently.
• Phone transcriptions: Help callers and receivers transcribe a conversation between 2 or more speakers with phone APIs .
• Interviews: Turn spoken words into a comprehensive speech log the interviewer can reference later with this software. When a journalist interviews someone, they may want to record it to be more active and attentive without risking misquotes.

Try Twilio’s Speech Recognition API

Speech-to-text applications help you connect to larger and more diverse audiences. But to deploy these capabilities at scale, you need flexible and affordable speech recognition technology—and that’s where we can help.

Twilio’s Speech Recognition API performs real-time translation and converts speech to text in 119 languages and dialects. Make your customer service more accessible on a pay-as-you-go plan, with no upfront fees and free support. Get started for free !

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Speech Recognition

Speech recognition is the capability of an electronic device to understand spoken words. A microphone records a person's voice and the hardware converts the signal from analog sound waves to digital audio. The audio data is then processed by software , which interprets the sound as individual words.

A common type of speech recognition is "speech-to-text" or "dictation" software, such as Dragon Naturally Speaking, which outputs text as you speak. While you can buy speech recognition programs, modern versions of the Macintosh and Windows operating systems include a built-in dictation feature. This capability allows you to record text as well as perform basic system commands.

In Windows, some programs support speech recognition automatically while others do not. You can enable speech recognition for all applications by selecting All Programs → Accessories → Ease of Access → Windows Speech Recognition and clicking "Enable dictation everywhere." In OS X, you can enable dictation in the "Dictation & Speech" system preference pane. Simply check the "On" button next to Dictation to turn on the speech-to-text capability. To start dictating in a supported program, select Edit → Start Dictation . You can also view and edit spoken commands in OS X by opening the "Accessibility" system preference pane and selecting "Speakable Items."

Another type of speech recognition is interactive speech, which is common on mobile devices, such as smartphones and tablets . Both iOS and Android devices allow you to speak to your phone and receive a verbal response. The iOS version is called "Siri," and serves as a personal assistant. You can ask Siri to save a reminder on your phone, tell you the weather forecast, give you directions, or answer many other questions. This type of speech recognition is considered a natural user interface (or NUI ), since it responds naturally to your spoken input .

While many speech recognition systems only support English, some speech recognition software supports multiple languages. This requires a unique dictionary for each language and extra algorithms to understand and process different accents. Some dictation systems, such as Dragon Naturally Speaking, can be trained to understand your voice and will adapt over time to understand you more accurately.

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How Does Speech Recognition Work? (9 Simple Questions Answered)

• by Team Experts
• July 2, 2023 July 3, 2023

Discover the Surprising Science Behind Speech Recognition – Learn How It Works in 9 Simple Questions!

Speech recognition is the process of converting spoken words into written or machine-readable text. It is achieved through a combination of natural language processing , audio inputs, machine learning , and voice recognition . Speech recognition systems analyze speech patterns to identify phonemes , the basic units of sound in a language. Acoustic modeling is used to match the phonemes to words , and word prediction algorithms are used to determine the most likely words based on context analysis . Finally, the words are converted into text.

What is Natural Language Processing and How Does it Relate to Speech Recognition?

How do audio inputs enable speech recognition, what role does machine learning play in speech recognition, how does voice recognition work, what are the different types of speech patterns used for speech recognition, how is acoustic modeling used for accurate phoneme detection in speech recognition systems, what is word prediction and why is it important for effective speech recognition technology, how can context analysis improve accuracy of automatic speech recognition systems, common mistakes and misconceptions.

Natural language processing (NLP) is a branch of artificial intelligence that deals with the analysis and understanding of human language. It is used to enable machines to interpret and process natural language, such as speech, text, and other forms of communication . NLP is used in a variety of applications , including automated speech recognition , voice recognition technology , language models, text analysis , text-to-speech synthesis , natural language understanding , natural language generation, semantic analysis , syntactic analysis, pragmatic analysis, sentiment analysis, and speech-to-text conversion. NLP is closely related to speech recognition , as it is used to interpret and understand spoken language in order to convert it into text.

Audio inputs enable speech recognition by providing digital audio recordings of spoken words . These recordings are then analyzed to extract acoustic features of speech, such as pitch, frequency, and amplitude. Feature extraction techniques , such as spectral analysis of sound waves, are used to identify and classify phonemes . Natural language processing (NLP) and machine learning models are then used to interpret the audio recordings and recognize speech. Neural networks and deep learning architectures are used to further improve the accuracy of voice recognition . Finally, Automatic Speech Recognition (ASR) systems are used to convert the speech into text, and noise reduction techniques and voice biometrics are used to improve accuracy .

Machine learning plays a key role in speech recognition , as it is used to develop algorithms that can interpret and understand spoken language. Natural language processing , pattern recognition techniques , artificial intelligence , neural networks, acoustic modeling , language models, statistical methods , feature extraction , hidden Markov models (HMMs), deep learning architectures , voice recognition systems, speech synthesis , and automatic speech recognition (ASR) are all used to create machine learning models that can accurately interpret and understand spoken language. Natural language understanding is also used to further refine the accuracy of the machine learning models .

Voice recognition works by using machine learning algorithms to analyze the acoustic properties of a person’s voice. This includes using voice recognition software to identify phonemes , speaker identification, text normalization , language models, noise cancellation techniques , prosody analysis , contextual understanding , artificial neural networks, voice biometrics , speech synthesis , and deep learning . The data collected is then used to create a voice profile that can be used to identify the speaker .

The different types of speech patterns used for speech recognition include prosody , contextual speech recognition , speaker adaptation , language models, hidden Markov models (HMMs), neural networks, Gaussian mixture models (GMMs) , discrete wavelet transform (DWT), Mel-frequency cepstral coefficients (MFCCs), vector quantization (VQ), dynamic time warping (DTW), continuous density hidden Markov model (CDHMM), support vector machines (SVM), and deep learning .

Acoustic modeling is used for accurate phoneme detection in speech recognition systems by utilizing statistical models such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). Feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs) are used to extract relevant features from the audio signal . Context-dependent models are also used to improve accuracy . Discriminative training techniques such as maximum likelihood estimation and the Viterbi algorithm are used to train the models. In recent years, neural networks and deep learning algorithms have been used to improve accuracy , as well as natural language processing techniques .

Word prediction is a feature of natural language processing and artificial intelligence that uses machine learning algorithms to predict the next word or phrase a user is likely to type or say. It is used in automated speech recognition systems to improve the accuracy of the system by reducing the amount of user effort and time spent typing or speaking words. Word prediction also enhances the user experience by providing faster response times and increased efficiency in data entry tasks. Additionally, it reduces errors due to incorrect spelling or grammar, and improves the understanding of natural language by machines. By using word prediction, speech recognition technology can be more effective , providing improved accuracy and enhanced ability for machines to interpret human speech.

Context analysis can improve the accuracy of automatic speech recognition systems by utilizing language models, acoustic models, statistical methods , and machine learning algorithms to analyze the semantic , syntactic, and pragmatic aspects of speech. This analysis can include word – level , sentence- level , and discourse-level context, as well as utterance understanding and ambiguity resolution. By taking into account the context of the speech, the accuracy of the automatic speech recognition system can be improved.

• Misconception : Speech recognition requires a person to speak in a robotic , monotone voice. Correct Viewpoint: Speech recognition technology is designed to recognize natural speech patterns and does not require users to speak in any particular way.
• Misconception : Speech recognition can understand all languages equally well. Correct Viewpoint: Different speech recognition systems are designed for different languages and dialects, so the accuracy of the system will vary depending on which language it is programmed for.
• Misconception: Speech recognition only works with pre-programmed commands or phrases . Correct Viewpoint: Modern speech recognition systems are capable of understanding conversational language as well as specific commands or phrases that have been programmed into them by developers.

Speech Recognition: Definition, Importance and Uses

Transkriptor 2024-01-17

Speech recognition, known as voice recognition or speech-to-text, is a technological development that converts spoken language into written text. It has two main benefits, these include enhancing task efficiency and increasing accessibility for everyone including individuals with physical impairments.

The alternative of speech recognition is manual transcription. Manual transcription is the process of converting spoken language into written text by listening to an audio or video recording and typing out the content.

There are many speech recognition software, but a few names stand out in the market when it comes to speech recognition software; Dragon NaturallySpeaking, Google's Speech-to-Text and Transkriptor.

The concept behind "what is speech recognition?" pertains to the capacity of a system or software to understand and transform oral communication into written textual form. It functions as the fundamental basis for a wide range of modern applications, ranging from voice-activated virtual assistants such as Siri or Alexa to dictation tools and hands-free gadget manipulation.

The development is going to contribute to a greater integration of voice-based interactions into an individual's everyday life.

What is Speech Recognition?

Speech recognition, known as ASR, voice recognition or speech-to-text, is a technological process. It allows computers to analyze and transcribe human speech into text.

How does Speech Recognition work?

Speech recognition technology works similar to how a person has a conversation with a friend. Ears detect the voice, and the brain processes and understands.The technology does, but it involves advanced software as well as intricate algorithms. There are four steps to how it works.

The microphone records the sounds of the voice and converts them into little digital signals when users speak into a device. The software processes the signals to exclude other voices and enhance the primary speech. The system breaks down the speech into small units called phonemes.

Different phonemes give their own unique mathematical representations by the system. It is able to differentiate between individual words and make educated predictions about what the speaker is trying to convey.

The system uses a language model to predict the right words. The model predicts and corrects word sequences based on the context of the speech.

The textual representation of the speech is produced by the system. The process requires a short amount of time. However, the correctness of the transcription is contingent on a variety of circumstances including the quality of the audio.

What is the importance of Speech Recognition?

The importance of speech recognition is listed below.

• Efficiency: It allows for hands-free operation. It makes multitasking easier and more efficient.
• Accessibility: It provides essential support for people with disabilities.
• Safety: It reduces distractions by allowing hands-free phone calls.
• Real-time translation: It facilitates real-time language translation. It breaks down communication barriers.
• Automation: It powers virtual assistants like Siri, Alexa, and Google Assistant, streamlining many daily tasks.
• Personalization: It allows devices and apps to understand user preferences and commands.

What are the Uses of Speech Recognition?

The 7 uses of speech recognition are listed below.

• Virtual Assistants. It includes powering voice-activated assistants like Siri, Alexa, and Google Assistant.
• Transcription services. It involves converting spoken content into written text for documentation, subtitles, or other purposes.
• Healthcare. It allows doctors and nurses to dictate patient notes and records hands-free.
• Automotive. It covers enabling voice-activated controls in vehicles, from playing music to navigation.
• Customer service. It embraces powering voice-activated IVRs in call centers.
• Educatio.: It is for easing in language learning apps, aiding in pronunciation, and comprehension exercises.
• Gaming. It includes providing voice command capabilities in video games for a more immersive experience.

Who Uses Speech Recognition?

General consumers, professionals, students, developers, and content creators use voice recognition software. Voice recognition sends text messages, makes phone calls, and manages their devices with voice commands. Lawyers, doctors, and journalists are among the professionals who employ speech recognition. Using speech recognition software, they dictate domain-specific information.

What is the Advantage of Using Speech Recognition?

The advantage of using speech recognition is mainly its accessibility and efficiency. It makes human-machine interaction more accessible and efficient. It reduces the human need which is also time-consuming and open to mistakes.

It is beneficial for accessibility. People with hearing difficulties use voice commands to communicate easily. Healthcare has seen considerable efficiency increases, with professionals using speech recognition for quick recording. Voice commands in driving settings help maintain safety and allow hands and eyes to focus on essential duties.

What is the Disadvantage of Using Speech Recognition?

The disadvantage of using speech recognition is its potential for inaccuracies and its reliance on specific conditions. Ambient noise or  accents confuse the algorithm. It results in misinterpretations or transcribing errors.

These inaccuracies are problematic. They are crucial in sensitive situations such as medical transcribing or legal documentation. Some systems need time to learn how a person speaks in order to work correctly. Voice recognition systems probably have difficulty interpreting multiple speakers at the same time. Another disadvantage is privacy. Voice-activated devices may inadvertently record private conversations.

What are the Different Types of Speech Recognition?

The 3 different types of speech recognition are listed below.

• Automatic Speech Recognition (ASR)
• Speaker-Dependent Recognition (SDR)
• Speaker-Independent Recognition (SIR)

Automatic Speech Recognition (ASR) is one of the most common types of speech recognition . ASR systems convert spoken language into text format. Many applications use them like Siri and Alexa. ASR focuses on understanding and transcribing speech regardless of the speaker, making it widely applicable.

Speaker-Dependent recognition recognizes a single user's voice. It needs time to learn and adapt to their particular voice patterns and accents. Speaker-dependent systems are very accurate because of the training. However, they struggle to recognize new voices.

Speaker-independent recognition interprets and transcribes speech from any speaker. It does not care about the accent, speaking pace, or voice pitch. These systems are useful in applications with many users.

What Accents and Languages Can Speech Recognition Systems Recognize?

The accents and languages that speech recognition systems can recognize are English, Spanish, and Mandarin to less common ones. These systems frequently incorporate customized models for distinguishing dialects and accents. It recognizes the diversity within languages. Transkriptor, for example, as a dictation software, supports over 100 languages.

Is Speech Recognition Software Accurate?

Yes, speech recognition software is accurate above 95%. However, its accuracy varies depending on a number of things. Background noise and audio quality are two examples of these.

How Accurate Can the Results of Speech Recognition Be?

Speech recognition results can achieve accuracy levels of up to 99% under optimal conditions. The highest level of speech recognition accuracy requires controlled conditions such as audio quality and background noises. Leading speech recognition systems have reported accuracy rates that exceed 99%.

How Does Text Transcription Work with Speech Recognition?

Text transcription works with speech recognition by analyzing and processing audio signals. Text transcription process starts with a microphone that records the speech and converts it to digital data. The algorithm then divides the digital sound into small pieces and analyzes each one to identify its distinct tones.

Advanced computer algorithms aid the system for matching these sounds to recognized speech patterns. The software compares these patterns to a massive language database to find the words users articulated. It then brings the words together to create a logical text.

How are Audio Data Processed with Speech Recognition?

Speech recognition processes audio data by splitting sound waves, extracting features, and mapping them to linguistic parts. The system collects and processes continuous sound waves when users speak into a device. The software advances to the feature extraction stage.

The software isolates specific features of the sound. It focuses on phonemes that are crucial for identifying one phoneme from another. The process entails evaluating the frequency components.

The system then starts using its trained models. The software combines the extracted features to known phonemes by using vast databases and machine learning models.

The system takes the phonemes, and puts them together to form words and phrases. The system combines technology skills and language understanding to convert noises into intelligible text or commands.

What is the best speech recognition software?

The 3 best speech recognition software are listed below.

Transkriptor

• Dragon NaturallySpeaking
• Google's Speech-to-Text

However, choosing the best speech recognition software depends on personal preferences.

Transkriptor is an online transcription software that uses artificial intelligence for quick and accurate transcription. Users are able to translate their transcripts with a single click right from the Transkriptor dashboard. Transkriptor technology is available in the form of a smartphone app, a Google Chrome extension, and a virtual meeting bot. It is compatible with popular platforms like Zoom, Microsoft Teams, and Google Meet which makes it one of the Best Speech Recognition Software.

Dragon NaturallySpeaking allows users to transform spoken speech into written text. It offers accessibility as well as adaptations for specific linguistic languages. Users like software’s adaptability for different vocabularies.

Google's Speech-to-Text is widely used for its scalability, integration options, and ability to support multiple languages. Individuals use it in a variety of applications ranging from transcription services to voice-command systems.

Is Speech Recognition and Dictation the Same?

No, speech recognition and dictation are not the same. Their principal goals are different, even though both voice recognition and dictation make conversion of spoken language into text. Speech recognition is a broader term covering the technology's ability to recognize and analyze spoken words. It converts them into a format that computers understand.

Dictation refers to the process of speaking aloud for recording. Dictation software uses speech recognition to convert spoken words into written text.

What is the Difference between Speech Recognition and Dictation?

The difference between speech recognition and dictation are related to their primary purpose, interactions, and scope. Itss primary purpose is to recognize and understand spoken words. Dictation has a more definite purpose. It focuses on directly transcribing spoken speech into written form.

Speech Recognition covers a wide range of applications in terms of scope. It helps voice assistants respond to user questions. Dictation has a narrower scope.

It provides a more dynamic interactive experience, often allowing for two-way dialogues. For example, virtual assistants such as Siri or Alexa not only understand user requests but also provide feedback or answers. Dictation works in a more basic fashion. It's typically a one-way procedure in which the user speaks and the system transcribes without the program engaging in a response discussion.

Frequently Asked Questions

Transkriptor stands out for its ability to support over 100 languages and its ease of use across various platforms. Its AI-driven technology focuses on quick and accurate transcription.

Yes, modern speech recognition software is increasingly adept at handling various accents. Advanced systems use extensive language models that include different dialects and accents, allowing them to accurately recognize and transcribe speech from diverse speakers.

Speech recognition technology greatly enhances accessibility by enabling voice-based control and communication, which is particularly beneficial for individuals with physical impairments or motor skill limitations. It allows them to operate devices, access information, and communicate effectively.

Speech recognition technology's efficiency in noisy environments has improved, but it can still be challenging. Advanced systems employ noise cancellation and voice isolation techniques to filter out background noise and focus on the speaker's voice.

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From Talk to Tech: Exploring the World of Speech Recognition

What is Speech Recognition Technology?

Imagine being able to control electronic devices, order groceries, or dictate messages with just voice. Speech recognition technology has ushered in a new era of interaction with devices, transforming the way we communicate with them. It allows machines to understand and interpret human speech, enabling a range of applications that were once thought impossible.

Speech recognition leverages machine learning algorithms to recognize speech patterns, convert audio files into text, and examine word meaning. Siri, Alexa, Google's Assistant, and Microsoft's Cortana are some of the most popular speech to text voice assistants used today that can interpret human speech and respond in a synthesized voice.

From personal assistants that can understand every command directed towards them to self-driving cars that can comprehend voice instructions and take the necessary actions, the potential applications of speech recognition are manifold. As technology continues to advance, the possibilities are endless.

How do Speech Recognition Systems Work?

speech to text processing is traditionally carried out in the following way:

Recording the audio:  The first step of speech to text conversion involves recording the audio and voice signals using a microphone or other audio input devices.

Breaking the audio into parts: The recorded voice or audio signals are then broken down into small segments, and features are extracted from each piece, such as the sound's frequency, pitch, and duration.

Digitizing speech into computer-readable format:  In the third step, the speech data is digitized into a computer-readable format that identifies the sequence of characters to remember the words or phrases that were most likely spoken.

Decoding speech using the algorithm:  Finally, language models decode the speech using speech recognition algorithms to produce a transcript or other output.

To adapt to the nature of human speech and language, speech recognition is designed to identify patterns, speaking styles, frequency of words spoken, and speech dialects on various levels. Advanced speech recognition software are also capable of eliminating background noises that often accompany speech signals.

When it comes to processing human speech, the following two types of models are used:

Acoustic Models

Acoustic models are a type of machine learning model used in speech recognition systems. These models are designed to help a computer understand and interpret spoken language by analyzing the sound waves produced by a person's voice.

Language Models

Based on the speech context, language models employ statistical algorithms to forecast the likelihood of words and phrases. They compare the acoustic model's output to a pre-built vocabulary of words and phrases to identify the most likely word order that makes sense in a given context of the speech. 

Applications of Speech Recognition Technology

Automatic speech recognition is becoming increasingly integrated into our daily lives, and its potential applications are continually expanding. With the help of speech to text applications, it's now becoming convenient to convert a speech or spoken word into a text format, in minutes.

Speech recognition is also used across industries, including healthcare , customer service, education, automotive, finance, and more, to save time and work efficiently. Here are some common speech recognition applications:

Voice Command for Smart Devices

Today, there are many home devices designed with voice recognition. Mobile devices and home assistants like Amazon Echo or Google Home are among the most widely used speech recognition system. One can easily use such devices to set reminders, place calls, play music, or turn on lights with simple voice commands.

Online Voice Search

Finding information online is now more straightforward and practical, thanks to speech to text technology. With online voice search, users can search using their voice rather than typing. This is an excellent advantage for people with disabilities and physical impairments and those that are multitasking and don't have the time to type a prompt.

Help People with Disabilities

People with disabilities can also benefit from speech to text applications because it allows them to use voice recognition to operate equipment, communicate, and carry out daily duties. In other words, it improves their accessibility. For example, in case of emergencies, people with visual impairment can use voice commands to call their friends and family on their mobile devices.

Business Applications of Speech Recognition

Speech recognition has various uses in business, including banking, healthcare, and customer support. In these industries, voice recognition mainly aims at enhancing productivity, communication, and accessibility. Some common applications of speech technology in business sectors include:

Speech recognition is used in the banking industry to enhance customer service and expedite internal procedures. Banks can also utilize speech to text programs to enable clients to access their accounts and conduct transactions using only their voice.

Customers in the bank who have difficulties entering or navigating through complicated data will find speech to text particularly useful. They can simply voice search the necessary data. In fact, today, banks are automating procedures like fraud detection and customer identification using this impressive technology, which can save costs and boost security.

Voice recognition is used in the healthcare industry to enhance patient care and expedite administrative procedures. For instance, physicians can dictate notes about patient visits using speech recognition programs, which can then be converted into electronic medical records. This also helps to save a lot of time, and correct data is recorded in the best way possible with this technology.

Customer Support

Speech recognition is employed in customer care to enhance the customer experience and cut expenses. For instance, businesses can automate time-consuming processes using speech to text so that customers can access information and solve problems without speaking to a live representative. This could shorten wait times and increase customer satisfaction.

Challenges with Speech Recognition Technology

Although speech recognition has become popular in recent years and made our lives easier, there are still several challenges concerning speech recognition that needs to be addressed.

Accuracy may not always be perfect

A speech recognition software can still have difficulty accurately recognizing speech in noisy or crowded environments or when the speaker has an accent or speech impediment. This can lead to incorrect transcriptions and miscommunications.

The software can not always understand complexity and jargon

Any speech recognition software has a limited vocabulary, so it may struggle to identify uncommon or specialized vocabulary like complex sentences or technical jargon, making it less useful in specific industries or contexts. Errors in interpretation or translation may happen if the speech recognition fails to recognize the context of words or phrases.

Concern about data privacy, data can be recorded.

Speech recognition technology relies on recording and storing audio data, which can raise concerns about data privacy. Users may be uncomfortable with their voice recordings being stored and used for other purposes. Also, voice notes, phone calls, and recordings may be recorded without the user's knowledge, and hacking or impersonation can be vulnerable to these security breaches. These things raise privacy and security concerns.

Software that Use Speech Recognition Technology

Many software programs use speech recognition technology to transcribe spoken words into text. Here are some of the most popular ones:

Nuance Dragon.

Amazon Transcribe.

Google Text to Speech

Watson Speech to Text

To sum up, speech recognition technology has come a long way in recent years. Given its benefits, including increased efficiency, productivity, and accessibility, its finding applications across a wide range of industries. As we continue to explore the potential of this evolving technology, we can expect to see even more exciting applications emerge in the future.

With the power of AI and machine learning at our fingertips, we're poised to transform the way we interact with technology in ways we never thought possible. So, let's embrace this exciting future and see where speech recognition takes us next!

What are the three steps of speech recognition?

The three steps of speech recognition are as follows:

Step 1: Capture the acoustic signal

The first step is to capture the acoustic signal using an audio input device and later pre-process the motion to remove noise and other unwanted sounds. The movement is then broken down into small segments, and features such as frequency, pitch, and duration are extracted from each piece.

Step 2: Combining the acoustic and language models

The second step involves combining the acoustic and language models to produce a transcription of the spoken words and word sequences.

Step 3: Converting the text into a synthesized voice

The final step is converting the text into a synthesized voice or using the transcription to perform other actions, such as controlling a computer or navigating a system.

What are examples of speech recognition?

Speech recognition is used in a wide range of applications. The most famous examples of speech recognition are voice assistants like Apple's Siri, Amazon's Alexa, and Google Assistant. These assistants use effective speech recognition to understand and respond to voice commands, allowing users to ask questions, set reminders, and control their smart home devices using only voice.

What is the importance of speech recognition?

Speech recognition is essential for improving accessibility for people with disabilities, including those with visual or motor impairments. It can also improve productivity in various settings and promote language learning and communication in multicultural environments. Speech recognition can break down language barriers, save time, and reduce errors.

You should also read:

Understanding Speech to Text in Depth

Top 10 Speech to Text Software in 2024

How Speech Recognition is Changing Language Learning

What is Speech Recognition? What are its Applications?

Speech recognition, also known as speech to text, is the ability of a machine or computer program to identify spoken words and convert them into readable text. Rudimentary forms of speech recognition software will only be able to recognize a limited range of vocabulary and phrases, while more advanced versions will be able to pick up complex speech in a variety of languages, accents, and dialects. Speech recognition is at the intersection of computer engineering, linguistics, and computer science. Many smartphone and computer devices on the market today come with some form of speech recognition technology built into their software.

It is important to note that while many people may use voice recognition and speech recognition as two interchangeable terms, they are in fact two distinct processes. While speech recognition is used to identify words in a particular spoken language, voice recognition aims to identify a speaker’s individual voice by using biometric technology. Moreover, speech recognition enables the hands-free control of various devices and equipment, creates print-ready diction, and gives input to auto-translation. Speech recognition is also used to enable popular personal assistants in smartphones and devices such as Apple’s Siri or Amazon’s Alexa.

How does speech recognition work?

Speech recognition works by using algorithms through a process referred to as language and acoustic modeling. Acoustic modeling is used to represent the relationship between audio signals and linguistic units of speech. Contrarily, language modeling matches sounds together with word sequences to help distinguish between similar-sounding words or phrases. Additionally, Hidden Makarov models or HMMs are often used to recognize certain temporal speech patterns and in turn improve accuracy within the system. An HMM is a statistical model that represents a randomly changing system, where it is assumed that future changes will not be dependent upon past changes.

Other methods used in speech recognition are natural language processing and N-grams. Natural language processing or NLP makes the overall speech recognition process easier and takes less time to institute. Alternatively, N-grams provide a relatively simple approach to language models and work by creating a probability distribution for a particular sequence. Finally, the most advanced speech recognition software will make use of state-of-the-art AI and machine learning technology.

What are the key features of effective speech recognition?

Many top-of-the-line speech recognition software options will allow users to adapt and customize the technology to their specific needs and requirements. Whether it be brand recognition or the nuances of a foreign language or speech, these software options make use of grammar, syntax, structure, and compositions of voice and audio signals to understand and process human speech. Examples of some of these features include:

• Language weighting – language weighting improves precision by weighting specific words that are spoken frequently (such as industry jargon or the name of a specific product) beyond terms used in everyday language.
• Speaker labeling – speaker labeling outputs a transcription that tags or cites a speaker’s individual contribution to a conversation with multiple participants
• Acoustics training – acoustics training will enable the system to adapt to an acoustic environment such as the ambient noise in a busy office setting. Furthermore, it will also pick up speaker styles like pace, volume, and voice pitch.
• Profanity filtering – profanity filtering can be used to identify and censor certain words in an attempt to sanitize speech output.

What are the applications for speech recognition?

The most frequent application of speech recognition today is for use in mobile devices. From voice dialing to asking Siri what the weather will be like on the upcoming Monday, voice recognition has become a key feature of many smartphone offerings currently on the market. Voice dialing, speech-to-text processing, call routing, and voice search features also function based upon speech recognition technology. Speech recognition can also be found in computer word processing programs such as google docs or Microsoft word, where users can change and dictate what they want to show up as text.

In the context of redaction software, speech recognition is used to automatically transcribe audio and video files . Products such as CaseGuard Studio will allow users to automatically transcribe hours of video and audio files in a matter of minutes. Moreover, this can be done in dozens of different languages with a multitude of stylistic choices. For instance, you may want to change the font or background color for the text on your transcription and captions as it appears in an online video.

• Transcription
• Translation

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What is Automatic Speech Recognition? A Comprehensive Overview of ASR Technology

This article aims to answer the question: What is ASR?, and provide a comprehensive overview of Automatic Speech Recognition technology.

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Automatic Speech Recognition, also known as ASR, is the use of Machine Learning or Artificial Intelligence (AI) technology to process human speech into readable text. The field has grown exponentially over the past decade, with ASR systems popping up in popular applications we use every day such as TikTok and Instagram for real-time captions, Spotify for podcast transcriptions, Zoom for meeting transcriptions, and more.

As ASR quickly approaches human accuracy levels, there will be an explosion of applications taking advantage of ASR technology in their products to make audio and video data more accessible. Already, Speech-to-Text APIs like AssemblyAI are making ASR technology more affordable, accessible, and accurate.

This article aims to answer the question: What is Automatic Speech Recognition (ASR)?, and to provide a comprehensive overview of Automatic Speech Recognition technology, including:

What is Automatic Speech Recognition (ASR)? - A Brief History

How asr works, asr key terms and features, key applications of asr, challenges of asr today, on the horizon for asr.

ASR as we know it extends back to 1952 when the infamous Bell Labs created “Audrey,” a digit recognizer. Audrey could only transcribe spoken numbers, but a decade later, researchers improved upon Audrey so that it could transcribe rudimentary spoken words like “hello”.

For most of the past fifteen years, ASR has been powered by classical Machine Learning technologies like Hidden Markov Models. Though once the industry standard, accuracy of these classical models had plateaued in recent years, opening the door for new approaches powered by advanced Deep Learning technology that’s also been behind the progress in other fields such as self-driving cars.

In 2014, Baidu published the paper, Deep Speech: Scaling up end-to-end speech recognition . In this paper, the researchers demonstrated the strength of applying Deep Learning research to power state-of-the-art, accurate speech recognition models. The paper kicked off a renaissance in the field of ASR, popularizing the Deep Learning approach and pushing model accuracy past the plateau and closer to human level.

Not only has accuracy skyrocketed, but access to ASR technology has also improved dramatically. Ten years ago, customers would have to engage in lengthy, expensive enterprise speech recognition software contracts to license ASR technology. Today, developers, startup companies, and Fortune 500s have access to state-of-the-art ASR technology via simple APIs like AssemblyAI’s Speech-to-Text API .

Let’s look more closely at these two dominant approaches to ASR.

Today, there are two main approaches to Automatic Speech Recognition: a traditional hybrid approach and an end-to-end Deep Learning approach.

Traditional Hybrid Approach

The traditional hybrid approach is the legacy approach to Speech Recognition and has dominated the field for the past fifteen years. Many companies still rely on this traditional hybrid approach simply because it’s the way it has always been done--there is more knowledge around how to build a robust model because of the extensive research and training data available, despite plateaus in accuracy.

Here’s how it works:

Traditional HMM and GMM systems

Traditional HMM (Hidden Markov Models) and GMM (Gaussian Mixture Models) require forced aligned data. Force alignment is the process of taking the text transcription of an audio speech segment and determining where in time particular words occur in the speech segment.

As you can see in the above illustration, this approach combines a lexicon model + an acoustic model + a language model to make transcription predictions.

Each step is defined in more detail below:

Lexicon Model

The lexicon model describes how words are pronounced phonetically. You usually need a custom phoneme set for each language, handcrafted by expert phoneticians.

Acoustic Model

The acoustic model (AM), models the acoustic patterns of speech. The job of the acoustic model is to predict which sound or phoneme is being spoken at each speech segment from the forced aligned data. The acoustic model is usually of an HMM or GMM variant.

Language Model

The language model (LM) models the statistics of language. It learns which sequences of words are most likely to be spoken, and its job is to predict which words will follow on from the current words and with what probability.

Decoding is a process of utilizing the lexicon, acoustic, and language model to produce a transcript.

Downsides of Using the Traditional Hybrid Approach

Though still widely used, the traditional hybrid approach to Speech Recognition does have a few drawbacks. Lower accuracy, as discussed previously, is the biggest. In addition, each model must be trained independently, making them time and labor intensive. Forced aligned data is also difficult to come by and a significant amount of human labor is needed, making them less accessible. Finally, experts are needed to build a custom phonetic set in order to boost the model’s accuracy.

End-to-End Deep Learning Approach

An end-to-end Deep Learning approach is a newer way of thinking about ASR, and how we approach ASR here at AssemblyAI.

How End-to-End Deep Learning Models Work

With an end-to-end system, you can directly map a sequence of input acoustic features into a sequence of words. The data does not need to be force-aligned. Depending on the architecture, a Deep Learning system can be trained to produce accurate transcripts without a lexicon model and language model, although language models can help produce more accurate results.

CTC, LAS, and RNNT

CTC, LAS, and RNNTs are popular Speech Recognition end-to-end Deep Learning architectures. These systems can be trained to produce super accurate results without needing force aligned data, lexicon models, and language models.

Advantages of End-to-End Deep Learning Models

End-to-end Deep Learning models are easier to train and require less human labor than a traditional approach. They are also more accurate than the traditional models being used today.

The Deep Learning research community is actively searching for ways to constantly improve these models using the latest research as well, so there’s no concern of accuracy plateaus any time soon--in fact, we’ll see Deep Learning models reach human level accuracy in the next few years.

Acoustic Model: The acoustic model takes in audio waveforms and predicts what words are present in the waveform.

Language Model : The language model can be used to help guide and correct the acoustic model's predictions.

Word Error Rate : The industry standard measurement of how accurate an ASR transcription is, as compared to a human transcription.

Speaker Diarization : Answers the question, who spoke when? Also referred to as speaker labels.

Custom Vocabulary : Also referred to as Word Boost, custom vocabulary boosts accuracy for a list of specific keywords or phrases when transcribing an audio file.

Sentiment Analysis : The sentiment, typically positive, negative, or neutral, of specific speech segments in an audio or video file.

See more models specific to AssemblyAI .

The immense advances in the field of ASR has seen a correlation of growth in Speech-to-Text APIs. Companies are using ASR technology for Speech-to-Text applications across a diverse range of industries. Some examples include:

Telephony: Call tracking , cloud phone solutions, and contact centers need accurate transcriptions, as well as innovative analytical features like Conversation Intelligence , call analytics, speaker diarization, and more.

Video Platforms: Real-time and asynchronous video captioning are industry standard. Video editing platforms (and video editors alike) also need content categorization and content moderation to improve accessibility and search.

Media Monitoring : Speech-to-Text APIs can help broadcast TV, podcasts, radio, and more quickly and accurately detect brand and other topic mentions for better advertising.

Virtual Meetings: Meeting platforms like Zoom, Google Meet, WebEx, and more need accurate transcriptions and the ability to analyze this content to drive key insights and action.

Choosing a Speech-to-Text API

With more APIs on the market, how do you know which Speech-to-Text API is best for your application ?

Key considerations to keep in mind include:

• How accurate the API is
• What additional models are offered
• What kind of support you can expect
• Pricing and documentation transparency
• Data security
• Company innovation

What Can I Build with Automatic Speech Recognition?

Automatic Speech Recognition models serve as a key component of any AI stack for companies that need to process and analyze spoken data.

For example, a Contact Center as a Service company is using highly accurate ASR to power smart transcription and speed up QA for its customers.

A call tracking company doubled its Conversational Intelligence customers by integrating AI-powered ASR into its platform and building powerful Generative AI products on top of the transcription data.

A qualitative data analysis platform added AI transcription to build a suite of AI-powered tools and features that resulted in 60% less time analyzing research data for its customers.

One of the main challenges of ASR today is the continual push toward human accuracy levels. While both ASR approaches--traditional hybrid and end-to-end Deep Learning--are significantly more accurate than ever before, neither can claim 100% human accuracy. This is because there is so much nuance in the way we speak, from dialects to slang to pitch. Even the best Deep Learning models can’t be trained to cover this long tail of edge cases without significant effort.

Some think they can solve this accuracy problem with custom Speech-to-Text models . However, unless you have a very specific use case, like children’s speech, custom models are actually less accurate, harder to train, and more expensive in practice than a good end-to-end Deep Learning model.

Another top concern is Speech-to-Text privacy for APIs . Too many large ASR companies use customer data to train models without explicit permission, raising serious concerns over data privacy. Continual data storage in the cloud also raises concerns over potential security breaches, especially if raw audio or video files or transcription text contains Personally Identifiable Information.

As the field of ASR continues to grow, we can expect to see greater integration of Speech-to-Text technology into our everyday lives, as well as more widespread industry applications.

We’re already seeing advancements in ASR and related AI fields taking place at an accelerated rate, such as OpenAI’s ChatGPT, HuggingFace spaces and ML apps , and AssemblyAI's Conformer-2, a state-of-the-art speech recognition model , trained on 1.1M hours of audio data.

In regards to model building, we also expect to see a shift to a self-supervised learning system to solve some of the challenges with accuracy discussed above.

End-to-end Deep Learning models are data hungry. Our Conformer-2 model at AssemblyAI, for example, is trained on 1.1 million hours of raw audio and video training data for industry-best accuracy levels. However, obtaining human transcriptions for this same training data would be almost impossible given the time constraints associated with human processing speeds.

This is where self-supervised deep learning systems can help. Essentially, this is a way to get an abundance of unlabeled data and build a foundational model on top of it. Then, since we have statistical knowledge of the data, we can fine-tune it on downstream tasks with a smaller amount of data, making it a more accessible approach to model building. This is an exciting possibility with profound implications for the field.

If this transition occurs, expect ASR models to become even more accurate and affordable, making their use and acceptance more widespread.

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Speech Recognition AI: What is it and How Does it Work|Gnani

A Beginner’s Guide to Speech Recognition AI

AI speech recognition is a technology that allows computers and applications to understand human speech data . It is a feature that has been around for decades, but it has increased in accuracy and sophistication in recent years.

Speech recognition works by using artificial intelligence to  recognize the words or language that a person speaks and then translate that content into text. It’s important to note that this technology is still in its infancy but is improving its accuracy rapidly.

What is Speech Recognition AI?

Speech recognition enables computers , applications and software to comprehend and translate human speech data into text  for business solutions . The speech recognition model works by using artificial intelligence (AI) to analyze your voice and language , identify by learning the words you are saying, and then output those words with transcription accuracy as model content or text data on a screen.

Speech Recognition in AI

Speech recognition is a significant part of artificial intelligence (AI) applications . AI is a machine’s ability to mimic human behaviour by learning from its environment. Speech recognition enables computers and software applications to “understand” what people are saying, which allows them to process information faster and with high accuracy. Speech recognition is also used as models in voice assistants like Siri and Alexa, which allow users to interact with computers using natural transcription language data or content .

Thanks to recent advancements, speech recognition technology is now more precise and widely used than in the past. It is used in various fields, including healthcare, customer service, education, and entertainment. However, there are still challenges to overcome, such as better handling of accents and dialects and the difficulty of recognizing speech in noisy environments. Despite these challenges, speech recognition is an exciting area of artificial intelligence with great potential for future development.

How Does Speech Recognition AI Work?

Speech recognition or voice recognition is a complex process that involves audio accuracy over several steps and data or language solutions , including:

• Recognizing the words , models and content in the user’s speech or audio . This business accuracy step requires training the model to identify each word in your vocabulary or audio cloud .
• Converting those audios and language into text. This step involves converting recognized audios i nto letters or numbers (called phonemes) so that other parts of the AI software solutions system can process th ose models .
• Determining what was said. Next, AI looks at which content and words were spoken most often and how frequently they were used together to determine their meaning (this process is known as “predictive modelling”).
• Parsing out commands from the rest of your speech or audio content (also known as disambiguation).

Speech Recognition AI and Natural Language Processing

Natural Language Processing is a part of artificial intelligence that involves analyzing data related to natural language and converting it into a machine- comprehendible format. Speech recognition and AI play a pivotal role in NLPs in improving the accuracy and efficiency of human language recognition. 

A lot of businesses now include speech-to-text software or speech recognition AI to enhance their business applications and improve customer experience. By using speech recognition AI and natural language processing together, companies can transcribe calls, meetings etc. Giant companies like Apple, Google, and Amazon are leveraging AI-based speech or voice recognition applications to provide a flawless customer experience. 

Use Cases of Speech Recognition AI

Speech recognition AI is being used as business solutions in many industries and applications . From ATMs to call centers and voice-activated audio content assistants, AI is helping people interact with technology and software more naturally with better data transcription accuracy than ever before.

Call Centers

Speech recognition is one of the most popular uses of speech AI in call centers. This technology allows you to listen to what customers are saying and then use that information via cloud models to respond appropriately.

You can also use speech recognition technology for voice or audio biometrics, which means using voice patterns as proof of identity or authorization for access solutions or services without relying on passwords or other traditional methods or models like fingerprints or eye scans. This can eliminate business issues like forgotten passwords or compromised security codes in favor of something more secure: your voice!

Banking and financial institutions are using speech AI applications to help customers with their business queries. For example, you can ask a bank about your account balance or the current interest rate on your savings account. This cuts down on the time it takes for customer service representatives to answer questions they would typically have to research and look at cloud data , which means quicker response times and better customer service.

Telecommunications

Speech-enabled AI is a technology that’s gaining traction in the telecommunications industry. Speech recognition technology models enable calls to be analyzed and managed more efficiently. This allows agents to focus on their highest-value tasks to deliver better customer service.

Customers can now interact with businesses in real-time 24/7 via voice transcription solutions or text messaging applications , which makes them feel more connected with the company and improves their overall experience.

Speech AI is a learning technology used in many different areas as transcription solutions . Healthcare is one of the most important, as it can help doctors and nurses care for their patients better. Voice-activated devices use learning models that allow patients to communicate with doctors, nurses, and other healthcare professionals without using their hands or typing on a keyboard.

Doctors can use speech recognition AI via cloud data to help patients understand their feelings and why they feel that way. It’s much easier than having them read through a brochure or pamphlet—and it’s more engaging. Speech AI can also take down patient histories and help with medical transcriptions.

Media and Marketing

Tools such as dictation software use speech recognition and AI to help users type or write more in much less time. Roughly speaking, copywriters and content writers can transcribe as much as 3000-4000 words in as less as half an hour on an average.

Accuracy, though, is a factor. These tools don’t guarantee 100% foolproof transcription. Still, they are extremely beneficial in helping media and marketing people in composing their first drafts.

Challenges in Working with Speech Recognition AI

There are many challenges in working with speech AI. For example, both technology and cloud are new and developing rapidly. As a result, it isn’t easy to make accurate predictions about how long it will take for a company to build its speech-enabled product.

Another challenge with speech AI is getting the right tools to analyze your data. Most people need access to this technology or cloud , so finding the right tool for your requirements may take time and effort.

You must use the correct language and syntax when creating your algorithms on cloud . This can be difficult because it requires understanding how computers and humans communicate. Speech recognition still needs improvement, and it can be difficult for computers to understand every word you say.

If you use speech recognition software, you will need to train it on your voice before it can understand what you’re saying. This can take a long time and requires careful study of how your voice sounds different from other people’s.

The other concern is that there are privacy laws surrounding medical records. These laws vary from state to state, so you’ll need to check with your jurisdiction before implementing speech AI technology.

Educating your staff on the technology and how it works is important if you decide to use speech AI. This will help them understand what they’re recording and why they’re recording it.

Frequently Asked Questions

How does speech recognition work.

Speech recognition AI is the process of converting spoken language into text. The technology uses machine learning and neural networks to process audio data and convert it into words that can be used in businesses.

What is the purpose of speech recognition AI ?

Speech recognition AI can be used for various purposes, including dictation and transcription. The technology is also used in voice assistants like Siri and Alexa.

What is speech communication in AI?

Speech communication is using speech recognition and speech synthesis to communicate with a computer. Speech recognition can allow users to dictate text into a program, saving time compared to typing it out. Speech synthesis is used for chatbots and voice assistants  like Siri and Alexa.

Which type of AI is used in speech recognition?

AI and machine learning are used in advanced speech recognition software, which processes speech through grammar, structure, and syntax.

What are the difficulties in voice recognition AI in artificial intelligence?

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What Is Speech Recognition? The Future of Technology

The term “speech recognition” may sound like something out of a science fiction novel, but it is actually real.

Speech recognition software has been around for a while and can be found in many different types of devices. It is used in simple applications such as answering machines, where it is used to answer the phone and record messages.

It is also used in hands-free cell phones, GPS devices, toys that respond to voice commands, and even Google Search.

For those who are unfamiliar with speech recognition, there are some important things that you should know about the technology.

• What Is Speech Recognition?

Speech recognition technology is a type of artificial intelligence that involves understanding what a person says. It usually does this by looking at the words being said and then comparing them to a predefined list of acceptable phrases.

Speech recognition software has an extensive list of words and phrases programmed into it, including things like proper names, slang, numbers, letters from the alphabet, and other common phrases.

When a person speaks into a device that uses speech recognition software, the software will analyze what is being said and then compare it to the list of acceptable phrases.

If it finds a match, it will respond accordingly. If there is no match, the software may still be able to interpret what was said based on the context of the conversation.

• How Does Speech Recognition Work?

There are three primary components to speech recognition: the microphone, the software, and the language database.

The microphone is used to capture the sound of a person’s voice. The software takes that sound and breaks it down into individual words. The language database stores all of the information about the words and phrases that the software is looking for.

Once these three components are set up, they work together to decipher what a person has said and convert it into text. If the microphone picks up enough of the sound and if all of the pre-programmed rules have been met, then the words can be converted into text.

That processed text can then be used in a number of different ways, such as being displayed on a screen or being used to control a device ( Voice Recognition ).

• Various Algorithms Are Used in Speech Recognition

Natural Language Processing (NLP)

Natural language processing (NLP) is a field of computer science and linguistics that deals with the interactions between computers and human languages.

It involves programming computers to understand human language and to produce results that are understandable by humans.

This type of algorithm analyzes data and looks for the possible word choice. It then applies linguistics concepts, such as grammar and sentence structure, to complete your request.

N-gram Analysis

N-gram analysis looks at the usage of words that are “neighbors” to other words. For example, if the word “add” were followed by “ed,” then an n-gram analysis would also look at other words that are often preceded by “ad.”

It finds patterns in the way people talk and uses those patterns to provide predictive text suggestions.

Hidden Markov Model (HMM)

Hidden Markov Model (HMM) is a statistical technique for analyzing sequences of data. This type of model creates a chain of states, each with an associated probability so that the next state can be predicted from the current state.

Each system has many states, and there are usually overlapping chains so that transitions are not visible to outside observers.

The way this algorithm works is it converts speech to text by assigning probabilities to every possible character that might next follow any sequence of characters to predict what should come next.

First, it breaks up the spoken text into phonemes-basic sounds that represent an individual letter or symbol in written language and then assigns probabilities to each one.

One example is the word “receive,” which is often mispronounced and not written in text messages. The term has several sounds that can be associated with the following characters: “C, C E I, E A U.”

Hidden Markov Model (HMM) calculates the probability of each sound represented by these letters to determine the appropriate word choice. It then applies probabilities to each character after “receive.”

Speaker Diarisation

This is the process of identifying and separating the individual voices in a group conversation. It is used to determine who is saying what so that the text can be attributed to the correct speaker.

It is used, for instance, to decide which transcript should be selected when there are multiple transcripts available, each with its own speaker label, automatically determining who spoke what contributes to making automatic speech recognition systems more accurate by allowing it to make decisions based on more than one voice sample.

Neural Networks

Neural networks are sophisticated software algorithms that can “learn” to recognize patterns in data. They are modeled after the brain and consist of many interconnected processing nodes, or neurons, that can “train” themselves to recognize specific patterns.

When you speak into a microphone, your voice is converted into digital form by a process called sampling. This involves measuring the amplitude (volume) and frequency (pitch) of the sound waves at fixed intervals-usually every 20 milliseconds-and recordings them as digital data.

The data is then sent to a neural network, which “reads” it and compares it to the templates stored in its memory. If it finds a match, it will report that you said a specific word or phrase.

Some computing tasks require the computer to ask for repetition. This involves using voice recognition software to select an alternative from among two possibilities, such as yes and no, and requesting clarification when necessary.

For example: “Did you say ‘yes’?”

• Examples of Speech Recognition

Speech Recognition is an accurate tool when it comes to communication. One example of this would be using your voice along with a written word to send a message on your phone since typing can get a bit tedious at times.

There are many ways that speech recognition is used for managing audio or video files. One way would be to transcribe audio or video files into text, which could be useful for accessibility purposes, like closed captioning.

The software can also correct grammar errors automatically after recognizing the mistake in the spoken word during transcription, like using “s” instead of “z.”

Speech recognition is a very complicated process. It all starts with converting human speech into digital data and then trying to figure out what was said.

For this, there are several things that need to be considered, such as the correct pronunciation of each word, which words should be grouped together since they can sound similar, and much more.

Once the speech has been converted into data, it is put through a series of algorithms to determine what was said. These are called Hidden Markov Model (HMM), Neural Networks, and Speaker Diarisation.

It all comes down to probabilities when it comes to speech recognition. The higher the probability that something will happen means there’s a better chance that it actually will. This is how the computer can figure out what you said, even if it’s not a word that is in its vocabulary.

There are many different ways to use speech recognition, and it is becoming more accurate every day. Some of the most common uses are dictation and transcription. With more people using speech recognition, the technology is only going to get better.

So far, it has been very successful in recognizing different accents and voices. As long as there is a good data connection, speech recognition can be used almost anywhere.

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Speech Recognition Definition | What Is Speech Recognition

In this article, you’ll learn what speech recognition is, types, applications, and how does speech recognition work.

📌 Table of Contents

• Speech recognition definition
• How does speech recognition work?
• Types of speech recognition
• Applications of speech recognition

What is Speech Recognition?

Speech Recognition is also known as “Speech-to-text” when a machine or computer program identifies a human’s spoken words and converts them into text format. Speech Recognition technology enables various devices to understand the command through human spoken words and automatically translate it into text. In contrast, voice recognition is a biometric technology that identifies a specific person’s voice. 

How Does Speech Recognition Work?

Speech Recognition technology is mainly used to convert a person’s spoken words into text for machine understanding. 

Speech Recognition can be divided into three categories:

• Automatic speech recognition (ASR): It’s used to transcribed the audios 
• Natural Language Processing (NLP): Deriving meaning from speech data and the subsequent transcribed text
• Text-to-Speech: The speech Recognition system converts text into human-like speech

Speech Recognition begins with digitizing a recorded speech with ASR and breaking the voice into segments of several tons in the form of spectrograms. Each spectrogram is analyzed and transcribed based on the NLP algorithm to predict the probability of words. Algorithms start to consider both a human’s spoken words and knowledge to understand the best possible command and analyze using TTS.

In simple words, the speech recognition software analyzes the person’s spoken words, breaks the speech into bits and converts it into a digital format for better understanding and then responds best possible based on similar patterns. 

What are the Types of Speech Recognition?

Speech recognition can be Separated into different types of speech recognition. Mainly there are five types of speech recognition:

1. Speaker-dependent system

A speaker-dependent system must be developed specifically for a single speaker. This type of system is easier to develop, cheaper and more accurate to run more smoothly, accurately and efficiently. A computer must be pre-trained before the speaker’s voice is understood more effectively. 

In this speaker-dependent system, the system is trained by repeating the vocabulary of words like pre-built templates. When a speaker-dependent system analyzes the human’s spoken words, it executes the command if it matches with the system.

2. Speaker independent system

It’s a speaker-independent system, so it executes commands based on analyzing the audio and converts into machine format to execute commands. There’s no prebuilt information saved in these independent systems because any human’s audio can be given to this system, and based on the algorithm, it performs the command.

Whenever audio is provided in a speaker-independent system, it’s converted into words for machine understanding and matched with related words to proceed with the command.

3. Discrete speech recognition

In discrete speech recognition, the speaker must pause between each word so that the speech recognition system can identify each word separately and execute accordingly.

4. Continuous speech recognition  

This type of audio is a normal rate of speaking that speech recognition systems can easily understand.

5. Natural Language

Natural language is like humans communicating with computers, and computers recognize the words without using vocabulary. In the speech recognition system, NLU is used to understand the human spoken queries and answers the questions.  

Application of Speech Recognition

Some of the Popular speech recognition digital assistance are:

• Amazon’s Alexa
• Apple’s Siri
• Google’s Google Assistant
• Microsoft’s Cortana

These are the most popular virtual assistants, and surely you’re familiar with their concept of voice command and communication with AI. 

Speech Recognition has wider use cases in different sectors like banking, marketing, healthcare, IoT, customer services, etc. Let’s discuss this in detail.

The main aim of using a speech recognition system in banking is to handle the customer’s queries. It’s cost-effective and reduces the need for employee costs. It’s a virtual assistant that helps customers know about their banking details, balance, transactions and payments history by asking a voice assistant. Customers will get answers instantly to their queries and boost customers satisfaction and loyalty. 

2. Marketing

The demand for voice search is increasing rapidly, and businesses are starting to understand the data and find potential customers to stay ahead of the trend. So marketers should shift their focus to sharing auditory information with their customers for better results.

3. Healthcare

Healthcare is one of the important sectors where the demand for virtual assistants is higher. 

Virtual assistants in the healthcare industry can be beneficial for:

• Quickly find the information from records
• Remind medicines, operations and other instructions to nurses and other staff
• Virtual assistants are useful for a consultation to know more details about common diseases and for guidance
• Work faster and less paperwork

4. Internet of Things (IoT )

You’ve noticed that Virtual assistants like Alexa, Siri, and Google Home are now connected with smart homes and control many devices like lighting, AC, TV etc., which can be easily accessible via voice command. This happens because of speech recognition technology used in IoT. And the growth of speech recognition technology is growing, and soon you’ll see cars connected with voice commands and many other inventions.

5. Security

Voice biometrics is one of the safest security systems that use a specific person’s voice as a password to unlock. Some big places at this level of security need to use voice biometric technology.

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Speech Recognition: How it works and what it is made of

Written by Aurélien Chapuzet

Discover | speech recognition, large language models and chatgpt, build a custom voice command in 5 easy steps, the things that smart eyewear lacks and are preventing it from rising to the top.

Speech recognition is a proven technology. Indeed, voice interfaces and voice assistants are now more powerful than ever and are developing in many fields. This exponential and continuous growth is leading to a diversification of speech recognition applications and related technologies.

Currently, we are in an era governed by cognitive technologies where we find for instance virtual or augmented reality, visual recognition and speech recognition!

However, even if the “Voice Generation” are the most apt to conceptualize this technology because they are born in the middle of its expansion, many people talk about it, but few really know how it works and what solutions are available.

And it is for this very reason that we propose you to discover speech recognition in detail through this article. Of course, this is just the basics to understand the field of speech technologies, other articles in our blog cover some topics in more detail.

“Strength in numbers”: the components of speech recognition

For the following explanations, we assume that “speech recognition” corresponds to a complete cycle of voice use.

Speech recognition is based on the complementarity between several technologies from the same field. To present all this, we will detail each of them chronologically, from the moment the individual speaks, until the order is carried out.

It should be noted that the technologies presented below can be used independently of each other and cover a wide range of applications. We will come back to this later.

The wake word, activate speech recognition, with voice

The first step that initiates the whole process is called the wake word. The main purpose of this first technology in the cycle is to activate the user’s voice to detect the voice command he or she wishes to perform.

Here, it is literally a matter of “waking up” the system. Although there are other ways of proceeding to trigger the listening, keeping the use of the voice throughout the cycle is, in our opinion, essential. Indeed, it allows us to propose a linear experience with voice as the only interface.

The trigger keyword inherently has several interests with respect to the design of voice assistants .

In our context, one of the main fears about speech recognition is the protection of personal data related to audio recording. With the recent appearance of the GDPR (General Data Protection Regulation) , this fear regarding privacy and intimacy has been further amplified, leading to the creation of a treaty to regulate it.

This is why the trigger word is so important. By conditioning the voice recording phase with this action, as long as the trigger word has not been clearly identified, nothing is recorded theoretically. Yes, theoretically, because depending on the company’s data policy, everything is relative. To prevent this, embedded (offline) speech recognition is an alternative.

Once the activation is confirmed, only the sentences carrying the intent of the action to be performed will be recorded and analyzed to ensure the use case works.

To learn more about the Wake-up Word, we invite you to read our article on Google’s Wake-up Word and the best practices to find your own!

Speech to Text (STT) , identifying and transcribing voice into text

Once speech recognition is initiated with the trigger word, it is necessary to exploit the voice. To do this, it is first essential to record and digitize it with Speech to Text technology (also known as automatic speech recognition ).

During this stage, the voice is captured in sound frequencies (in the form of audio files, like music or any other noise) that can be used later.

Depending on the listening environment, sound pollution may or may not be present. In order to improve the recording of these frequencies and therefore their reliability, different treatments can be applied.

• Normalization to remove peaks and valleys in the frequencies in order to harmonize the whole.
• The removal of background noise to improve audio quality.
• The cutting of segments into phonemes (which are distinctive units within frequencies, expressed in thousandths of a second, allowing words to be distinguished from one another.

The frequencies, once recorded, can be analyzed in order to associate each phoneme with a word or a group of words to constitute a text. This step can be done in different ways, but one method in particular is the state of the art today: Machine Learning.

A sub-part of this technology is called Deep Learning: an algorithm recreating a neural network, capable of analyzing a large amount of information and building a database listing the associations between frequencies and words. Thus, each association will create a neuron that will be used to deduce new correspondences.

Therefore, the more information there is, the more precise the model is statistically speaking and capable of taking into account the general context to assign the best word according to the others already defined.

Limiting STT errors is essential to obtain the most reliable information to proceed with the next steps.

NLP (Natural Language Processing), translating human language into machine language

Once the previous steps have been completed, the textual data is sent directly to the NLP (Natural Language Processing) module. The main purpose of this technology is to analyze the sentence and extract a maximum of linguistic data.

To do this, it starts by associating tags to the words of the sentence, this is called tokenization. These are actually “tags” that are applied to each word in order to characterize it. For example, “Open” will be defined as the verb defining an action, “the” as the determinant referring to “ Voice Development Kit ” which is a proper noun but also a COD etc… and this for each element of the sentence.

Once these first elements have been identified, it is necessary to give meaning to the orders resulting from the speech recognition. This is why two complementary analyses are performed.

First of all, syntactic analysis aims to model the structure of the sentence. It is a question here of identifying the place of the words within the whole but also their relative position compared to the others in order to understand their relations.

To complete and finish, the semantic analysis which, once the nature and the position of the words are found, will try to understand their meaning individually but also when they are assembled in the sentence in order to translate a general intention of the user.

The importance of NLP in speech recognition lies in its ability to translate textual elements (i.e. words and sentences) into normalized orders, including meaning and intent, that can be interpreted by the associated artificial intelligence and carried out.

Artificial intelligence, a necessary ally of speech recognition

First of all, artificial intelligence, although integrated in the process of the previous technologies, is not always essential to achieve the use cases. Indeed, if we are talking about connected technologies (i.e. Cloud), AI will be useful. Especially since the complexity of some use cases, especially the information to be correlated to produce them, makes it mandatory.

For example, it is sometimes necessary to compare several pieces of information with actions to be carried out, integrations of external or internal services or databases to be consulted.

In other words, artificial intelligence is the use case itself, the concrete action that will result from the voice interface. Depending on the context of use and the nature of the order, the elements requested and the results given will be different.

Let’s take a case in point. Vivoka has created a connected motorcycle helmet that allows to use functionalities with the voice.  Different uses are available, such as using GPS or music.

The request “Take me to a gas station on the way” will return a normalized command to the artificial intelligence with the user’s intention:

• Context: Vehicle fuel type, Price preference (affects distance travelled)
• External services: Call the API of the GPS solution provider
• Action to be performed: Keep the current route, add a step on the route

Here, the intelligence used by our system will submit information and a request to an external service that has a specialized intelligence to send us back the result to operate on the user.

AI is therefore a key component in many situations. However, for embedded functionalities (i.e. offline), the needs are less, being closer to the realization of simple commands such as navigation on an interface or the reporting of actions . It is a question here of having specific use cases that do not require consulting multiple information.

TTS (Text to Speech) , voice to answer and inform the user

Finally, the TTS (Text-to-Speech) concludes the process. It corresponds to the feedback of the system which is expressed through a synthetic voice. In the same spirit as the Wake-up Word, it closes the speech recognition by answering vocally in order to keep the homogeneity of the conversational interface.

The voice synthesis is built from human voices and sounds diversified according to language, gender, age or mood. Synthetic voices are thus generated in real time to dictate words or sentences through a phonetic assembly.

This speech recognition technology is useful for communicating information to the user, a symbol of a complete human-machine interface and also of a well-designed user experience.

Similarly, it represents an important dimension of Voice Marketing because the synthesized voices can be customized to match the image of the brands that use it.

The different speech recognition solutions

The speech recognition market is a fast-moving environment. As use cases are constantly being born and reinvented with technological progress, the adoption of speech solutions is driving innovation and attracting many players.

Today on the market, we can count major categories of uses related to speech recognition. Among them, we can mention :

Voice assistants

We find the GAFAs and their multi-support virtual assistants (smart speaker, telephone, etc.) but also initiatives from other companies. The personalization of voice assistants is a trend on the fringe of the market domination by GAFA, where brands want to regain their technical governance.

For example, KSH and its connected motorcycle helmet are among those players with specific needs, both marketing and functional.

Professional voice interfaces

We are talking about productivity tools for employees. One of the fastest growing sectors is the supply chain with the pick-by-voice . This is a voice device that allows operators to use speech recognition to work more efficiently and safely (hands-free, concentration…). The voice commands are similar to reports of actions and confirmations of operations performed.

There are many possibilities for companies to gain in productivity. Some use cases already exist and others will be created.

Speech recognition software

Voice dictation, for example, is a tool that is already used by thousands of individuals, personally or professionally (DS Avocats for instance). It allows you to dictate text (whether emails or reports) at a rate of 180 words per minute, whereas manual input is on average 60 words per minute. The tool brings productivity and comfort to document creation through a voice transcription engine adapted to dictation.

Connected objects (Internet of Things IoT)

The IoT world is also fond of voice innovations. This often concerns navigation or device use functionalities. Whether it is home automation equipment or more specialized products such as connected mirrors, speech recognition promises great prospects.

As the more experienced among you will have understood, this article explains in a succinct and introductory way a complex technology and uses. Similarly, the tools we have presented are a specific design of speech technologies, not the norm, although they are the most common designs.

To learn more about speech recognition and its capabilities, we recommend you browse our blog for more information or contact us directly to discuss the matter!

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SpeechRecognition 3.10.3

pip install SpeechRecognition Copy PIP instructions

Released: Mar 30, 2024

Library for performing speech recognition, with support for several engines and APIs, online and offline.

Project links

• Open issues:

View statistics for this project via Libraries.io , or by using our public dataset on Google BigQuery

License: BSD License (BSD)

Author: Anthony Zhang (Uberi)

Tags speech, recognition, voice, sphinx, google, wit, bing, api, houndify, ibm, snowboy

Requires: Python >=3.8

Maintainers

Classifiers

• 5 - Production/Stable
• OSI Approved :: BSD License
• MacOS :: MacOS X
• Microsoft :: Windows
• POSIX :: Linux
• Python :: 3
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Multimedia :: Sound/Audio :: Speech
• Software Development :: Libraries :: Python Modules

Project description

UPDATE 2022-02-09 : Hey everyone! This project started as a tech demo, but these days it needs more time than I have to keep up with all the PRs and issues. Therefore, I’d like to put out an open invite for collaborators - just reach out at me @ anthonyz . ca if you’re interested!

Speech recognition engine/API support:

Quickstart: pip install SpeechRecognition . See the “Installing” section for more details.

To quickly try it out, run python -m speech_recognition after installing.

Project links:

Library Reference

The library reference documents every publicly accessible object in the library. This document is also included under reference/library-reference.rst .

See Notes on using PocketSphinx for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under reference/pocketsphinx.rst .

You have to install Vosk models for using Vosk. Here are models avaiable. You have to place them in models folder of your project, like “your-project-folder/models/your-vosk-model”

See the examples/ directory in the repository root for usage examples:

First, make sure you have all the requirements listed in the “Requirements” section.

The easiest way to install this is using pip install SpeechRecognition .

Otherwise, download the source distribution from PyPI , and extract the archive.

In the folder, run python setup.py install .

Requirements

To use all of the functionality of the library, you should have:

The following requirements are optional, but can improve or extend functionality in some situations:

The following sections go over the details of each requirement.

The first software requirement is Python 3.8+ . This is required to use the library.

PyAudio (for microphone users)

PyAudio is required if and only if you want to use microphone input ( Microphone ). PyAudio version 0.2.11+ is required, as earlier versions have known memory management bugs when recording from microphones in certain situations.

If not installed, everything in the library will still work, except attempting to instantiate a Microphone object will raise an AttributeError .

The installation instructions on the PyAudio website are quite good - for convenience, they are summarized below:

PyAudio wheel packages for common 64-bit Python versions on Windows and Linux are included for convenience, under the third-party/ directory in the repository root. To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the repository root directory .

PocketSphinx-Python (for Sphinx users)

PocketSphinx-Python is required if and only if you want to use the Sphinx recognizer ( recognizer_instance.recognize_sphinx ).

PocketSphinx-Python wheel packages for 64-bit Python 3.4, and 3.5 on Windows are included for convenience, under the third-party/ directory . To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the SpeechRecognition folder.

On Linux and other POSIX systems (such as OS X), follow the instructions under “Building PocketSphinx-Python from source” in Notes on using PocketSphinx for installation instructions.

Note that the versions available in most package repositories are outdated and will not work with the bundled language data. Using the bundled wheel packages or building from source is recommended.

Vosk (for Vosk users)

Vosk API is required if and only if you want to use Vosk recognizer ( recognizer_instance.recognize_vosk ).

You can install it with python3 -m pip install vosk .

You also have to install Vosk Models:

Here are models avaiable for download. You have to place them in models folder of your project, like “your-project-folder/models/your-vosk-model”

Google Cloud Speech Library for Python (for Google Cloud Speech API users)

Google Cloud Speech library for Python is required if and only if you want to use the Google Cloud Speech API ( recognizer_instance.recognize_google_cloud ).

If not installed, everything in the library will still work, except calling recognizer_instance.recognize_google_cloud will raise an RequestError .

According to the official installation instructions , the recommended way to install this is using Pip : execute pip install google-cloud-speech (replace pip with pip3 if using Python 3).

FLAC (for some systems)

A FLAC encoder is required to encode the audio data to send to the API. If using Windows (x86 or x86-64), OS X (Intel Macs only, OS X 10.6 or higher), or Linux (x86 or x86-64), this is already bundled with this library - you do not need to install anything .

Otherwise, ensure that you have the flac command line tool, which is often available through the system package manager. For example, this would usually be sudo apt-get install flac on Debian-derivatives, or brew install flac on OS X with Homebrew.

Whisper (for Whisper users)

Whisper is required if and only if you want to use whisper ( recognizer_instance.recognize_whisper ).

You can install it with python3 -m pip install SpeechRecognition[whisper-local] .

Whisper API (for Whisper API users)

The library openai is required if and only if you want to use Whisper API ( recognizer_instance.recognize_whisper_api ).

If not installed, everything in the library will still work, except calling recognizer_instance.recognize_whisper_api will raise an RequestError .

You can install it with python3 -m pip install SpeechRecognition[whisper-api] .

Troubleshooting

The recognizer tries to recognize speech even when i’m not speaking, or after i’m done speaking..

Try increasing the recognizer_instance.energy_threshold property. This is basically how sensitive the recognizer is to when recognition should start. Higher values mean that it will be less sensitive, which is useful if you are in a loud room.

This value depends entirely on your microphone or audio data. There is no one-size-fits-all value, but good values typically range from 50 to 4000.

Also, check on your microphone volume settings. If it is too sensitive, the microphone may be picking up a lot of ambient noise. If it is too insensitive, the microphone may be rejecting speech as just noise.

The recognizer can’t recognize speech right after it starts listening for the first time.

The recognizer_instance.energy_threshold property is probably set to a value that is too high to start off with, and then being adjusted lower automatically by dynamic energy threshold adjustment. Before it is at a good level, the energy threshold is so high that speech is just considered ambient noise.

The solution is to decrease this threshold, or call recognizer_instance.adjust_for_ambient_noise beforehand, which will set the threshold to a good value automatically.

The recognizer doesn’t understand my particular language/dialect.

Try setting the recognition language to your language/dialect. To do this, see the documentation for recognizer_instance.recognize_sphinx , recognizer_instance.recognize_google , recognizer_instance.recognize_wit , recognizer_instance.recognize_bing , recognizer_instance.recognize_api , recognizer_instance.recognize_houndify , and recognizer_instance.recognize_ibm .

For example, if your language/dialect is British English, it is better to use "en-GB" as the language rather than "en-US" .

The recognizer hangs on recognizer_instance.listen ; specifically, when it’s calling Microphone.MicrophoneStream.read .

This usually happens when you’re using a Raspberry Pi board, which doesn’t have audio input capabilities by itself. This causes the default microphone used by PyAudio to simply block when we try to read it. If you happen to be using a Raspberry Pi, you’ll need a USB sound card (or USB microphone).

Once you do this, change all instances of Microphone() to Microphone(device_index=MICROPHONE_INDEX) , where MICROPHONE_INDEX is the hardware-specific index of the microphone.

To figure out what the value of MICROPHONE_INDEX should be, run the following code:

This will print out something like the following:

Now, to use the Snowball microphone, you would change Microphone() to Microphone(device_index=3) .

Calling Microphone() gives the error IOError: No Default Input Device Available .

As the error says, the program doesn’t know which microphone to use.

To proceed, either use Microphone(device_index=MICROPHONE_INDEX, ...) instead of Microphone(...) , or set a default microphone in your OS. You can obtain possible values of MICROPHONE_INDEX using the code in the troubleshooting entry right above this one.

The program doesn’t run when compiled with PyInstaller .

As of PyInstaller version 3.0, SpeechRecognition is supported out of the box. If you’re getting weird issues when compiling your program using PyInstaller, simply update PyInstaller.

You can easily do this by running pip install --upgrade pyinstaller .

On Ubuntu/Debian, I get annoying output in the terminal saying things like “bt_audio_service_open: […] Connection refused” and various others.

The “bt_audio_service_open” error means that you have a Bluetooth audio device, but as a physical device is not currently connected, we can’t actually use it - if you’re not using a Bluetooth microphone, then this can be safely ignored. If you are, and audio isn’t working, then double check to make sure your microphone is actually connected. There does not seem to be a simple way to disable these messages.

For errors of the form “ALSA lib […] Unknown PCM”, see this StackOverflow answer . Basically, to get rid of an error of the form “Unknown PCM cards.pcm.rear”, simply comment out pcm.rear cards.pcm.rear in /usr/share/alsa/alsa.conf , ~/.asoundrc , and /etc/asound.conf .

For “jack server is not running or cannot be started” or “connect(2) call to /dev/shm/jack-1000/default/jack_0 failed (err=No such file or directory)” or “attempt to connect to server failed”, these are caused by ALSA trying to connect to JACK, and can be safely ignored. I’m not aware of any simple way to turn those messages off at this time, besides entirely disabling printing while starting the microphone .

On OS X, I get a ChildProcessError saying that it couldn’t find the system FLAC converter, even though it’s installed.

Installing FLAC for OS X directly from the source code will not work, since it doesn’t correctly add the executables to the search path.

Installing FLAC using Homebrew ensures that the search path is correctly updated. First, ensure you have Homebrew, then run brew install flac to install the necessary files.

To hack on this library, first make sure you have all the requirements listed in the “Requirements” section.

To install/reinstall the library locally, run python setup.py install in the project root directory .

Before a release, the version number is bumped in README.rst and speech_recognition/__init__.py . Version tags are then created using git config gpg.program gpg2 && git config user.signingkey DB45F6C431DE7C2DCD99FF7904882258A4063489 && git tag -s VERSION_GOES_HERE -m "Version VERSION_GOES_HERE" .

Releases are done by running make-release.sh VERSION_GOES_HERE to build the Python source packages, sign them, and upload them to PyPI.

To run all the tests:

Testing is also done automatically by TravisCI, upon every push. To set up the environment for offline/local Travis-like testing on a Debian-like system:

FLAC Executables

The included flac-win32 executable is the official FLAC 1.3.2 32-bit Windows binary .

The included flac-linux-x86 and flac-linux-x86_64 executables are built from the FLAC 1.3.2 source code with Manylinux to ensure that it’s compatible with a wide variety of distributions.

The built FLAC executables should be bit-for-bit reproducible. To rebuild them, run the following inside the project directory on a Debian-like system:

The included flac-mac executable is extracted from xACT 2.39 , which is a frontend for FLAC 1.3.2 that conveniently includes binaries for all of its encoders. Specifically, it is a copy of xACT 2.39/xACT.app/Contents/Resources/flac in xACT2.39.zip .

Please report bugs and suggestions at the issue tracker !

How to cite this library (APA style):

Zhang, A. (2017). Speech Recognition (Version 3.8) [Software]. Available from https://github.com/Uberi/speech_recognition#readme .

How to cite this library (Chicago style):

Zhang, Anthony. 2017. Speech Recognition (version 3.8).

Also check out the Python Baidu Yuyin API , which is based on an older version of this project, and adds support for Baidu Yuyin . Note that Baidu Yuyin is only available inside China.

Copyright 2014-2017 Anthony Zhang (Uberi) . The source code for this library is available online at GitHub .

SpeechRecognition is made available under the 3-clause BSD license. See LICENSE.txt in the project’s root directory for more information.

For convenience, all the official distributions of SpeechRecognition already include a copy of the necessary copyright notices and licenses. In your project, you can simply say that licensing information for SpeechRecognition can be found within the SpeechRecognition README, and make sure SpeechRecognition is visible to users if they wish to see it .

SpeechRecognition distributes source code, binaries, and language files from CMU Sphinx . These files are BSD-licensed and redistributable as long as copyright notices are correctly retained. See speech_recognition/pocketsphinx-data/*/LICENSE*.txt and third-party/LICENSE-Sphinx.txt for license details for individual parts.

SpeechRecognition distributes source code and binaries from PyAudio . These files are MIT-licensed and redistributable as long as copyright notices are correctly retained. See third-party/LICENSE-PyAudio.txt for license details.

SpeechRecognition distributes binaries from FLAC - speech_recognition/flac-win32.exe , speech_recognition/flac-linux-x86 , and speech_recognition/flac-mac . These files are GPLv2-licensed and redistributable, as long as the terms of the GPL are satisfied. The FLAC binaries are an aggregate of separate programs , so these GPL restrictions do not apply to the library or your programs that use the library, only to FLAC itself. See LICENSE-FLAC.txt for license details.

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