speech words recognition

Speech to Text - Voice Typing & Transcription

Take notes with your voice for free, or automatically transcribe audio & video recordings. secure, accurate & blazing fast..

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Dictate Notes

Start taking notes, on our online voice-enabled notepad right away, for free.

Transcribe Recordings

Automatically transcribe audios & videos - upload files from your device or link to an online resource (Drive, YouTube, TikTok and more).

Speechnotes is a reliable and secure web-based speech-to-text tool that enables you to quickly and accurately transcribe your audio and video recordings, as well as dictate your notes instead of typing, saving you time and effort. With features like voice commands for punctuation and formatting, automatic capitalization, and easy import/export options, Speechnotes provides an efficient and user-friendly dictation and transcription experience. Proudly serving millions of users since 2015, Speechnotes is the go-to tool for anyone who needs fast, accurate & private transcription. Our Portfolio of Complementary Speech-To-Text Tools Includes:

Voice typing - Chrome extension

Dictate instead of typing on any form & text-box across the web. Including on Gmail, and more.

Transcription API & webhooks

Speechnotes' API enables you to send us files via standard POST requests, and get the transcription results sent directly to your server.

Zapier integration

Combine the power of automatic transcriptions with Zapier's automatic processes. Serverless & codeless automation! Connect with your CRM, phone calls, Docs, email & more.

Android Speechnotes app

Speechnotes' notepad for Android, for notes taking on your mobile, battle tested with more than 5Million downloads. Rated 4.3+ ⭐

iOS TextHear app

TextHear for iOS, works great on iPhones, iPads & Macs. Designed specifically to help people with hearing impairment participate in conversations. Please note, this is a sister app - so it has its own pricing plan.

Audio & video converting tools

Tools developed for fast - batch conversions of audio files from one type to another and extracting audio only from videos for minimizing uploads.

Our Sister Apps for Text-To-Speech & Live Captioning

Complementary to Speechnotes

Reads out loud texts, files & web pages

Reads out loud texts, PDFs, e-books & websites for free

Speechlogger

Live Captioning & Translation

Live captions & translations for online meetings, webinars, and conferences.

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We do not provide human transcription services ourselves, but, we partnered with a UK company that does. Learn more on human transcription and the 10% discount .

Dictation Notepad

Start taking notes with your voice for free

Speech to Text online notepad. Professional, accurate & free speech recognizing text editor. Distraction-free, fast, easy to use web app for dictation & typing.

Speechnotes is a powerful speech-enabled online notepad, designed to empower your ideas by implementing a clean & efficient design, so you can focus on your thoughts. We strive to provide the best online dictation tool by engaging cutting-edge speech-recognition technology for the most accurate results technology can achieve today, together with incorporating built-in tools (automatic or manual) to increase users' efficiency, productivity and comfort. Works entirely online in your Chrome browser. No download, no install and even no registration needed, so you can start working right away.

Speechnotes is especially designed to provide you a distraction-free environment. Every note, starts with a new clear white paper, so to stimulate your mind with a clean fresh start. All other elements but the text itself are out of sight by fading out, so you can concentrate on the most important part - your own creativity. In addition to that, speaking instead of typing, enables you to think and speak it out fluently, uninterrupted, which again encourages creative, clear thinking. Fonts and colors all over the app were designed to be sharp and have excellent legibility characteristics.

Example use cases

• Voice typing
• Writing notes, thoughts
• Medical forms - dictate
• Transcribers (listen and dictate)

Transcription Service

Start transcribing

Fast turnaround - results within minutes. Includes timestamps, auto punctuation and subtitles at unbeatable price. Protects your privacy: no human in the loop, and (unlike many other vendors) we do NOT keep your audio. Pay per use, no recurring payments. Upload your files or transcribe directly from Google Drive, YouTube or any other online source. Simple. No download or install. Just send us the file and get the results in minutes.

• Transcribe interviews
• Captions for Youtubes & movies
• Auto-transcribe phone calls or voice messages
• Students - transcribe lectures
• Podcasters - enlarge your audience by turning your podcasts into textual content
• Text-index entire audio archives

Key Advantages

Speechnotes is powered by the leading most accurate speech recognition AI engines by Google & Microsoft. We always check - and make sure we still use the best. Accuracy in English is very good and can easily reach 95% accuracy for good quality dictation or recording.

Lightweight & fast

Both Speechnotes dictation & transcription are lightweight-online no install, work out of the box anywhere you are. Dictation works in real time. Transcription will get you results in a matter of minutes.

Super Private & Secure!

Super private - no human handles, sees or listens to your recordings! In addition, we take great measures to protect your privacy. For example, for transcribing your recordings - we pay Google's speech to text engines extra - just so they do not keep your audio for their own research purposes.

Health advantages

Typing may result in different types of Computer Related Repetitive Strain Injuries (RSI). Voice typing is one of the main recommended ways to minimize these risks, as it enables you to sit back comfortably, freeing your arms, hands, shoulders and back altogether.

Saves you time

Need to transcribe a recording? If it's an hour long, transcribing it yourself will take you about 6! hours of work. If you send it to a transcriber - you will get it back in days! Upload it to Speechnotes - it will take you less than a minute, and you will get the results in about 20 minutes to your email.

Saves you money

Speechnotes dictation notepad is completely free - with ads - or a small fee to get it ad-free. Speechnotes transcription is only $0.1/minute, which is X10 times cheaper than a human transcriber! We offer the best deal on the market - whether it's the free dictation notepad ot the pay-as-you-go transcription service.

Dictation - Free

• Online dictation notepad
• Voice typing Chrome extension

Dictation - Premium

• Premium online dictation notepad
• Premium voice typing Chrome extension
• Support from the development team

Transcription

$0.1 /minute.

• Pay as you go - no subscription
• Audio & video recordings
• Speaker diarization in English
• Generate captions .srt files
• REST API, webhooks & Zapier integration

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Privacy policy.

We at Speechnotes, Speechlogger, TextHear, Speechkeys value your privacy, and that's why we do not store anything you say or type or in fact any other data about you - unless it is solely needed for the purpose of your operation. We don't share it with 3rd parties, other than Google / Microsoft for the speech-to-text engine.

Privacy - how are the recordings and results handled?

- transcription service.

Our transcription service is probably the most private and secure transcription service available.

• HIPAA compliant.
• No human in the loop. No passing your recording between PCs, emails, employees, etc.
• Secure encrypted communications (https) with and between our servers.
• Recordings are automatically deleted from our servers as soon as the transcription is done.
• Our contract with Google / Microsoft (our speech engines providers) prohibits them from keeping any audio or results.
• Transcription results are securely kept on our secure database. Only you have access to them - only if you sign in (or provide your secret credentials through the API)
• You may choose to delete the transcription results - once you do - no copy remains on our servers.

- Dictation notepad & extension

For dictation, the recording & recognition - is delegated to and done by the browser (Chrome / Edge) or operating system (Android). So, we never even have access to the recorded audio, and Edge's / Chrome's / Android's (depending the one you use) privacy policy apply here.

The results of the dictation are saved locally on your machine - via the browser's / app's local storage. It never gets to our servers. So, as long as your device is private - your notes are private.

Payments method privacy

The whole payments process is delegated to PayPal / Stripe / Google Pay / Play Store / App Store and secured by these providers. We never receive any of your credit card information.

More generic notes regarding our site, cookies, analytics, ads, etc.

• We may use Google Analytics on our site - which is a generic tool to track usage statistics.
• We use cookies - which means we save data on your browser to send to our servers when needed. This is used for instance to sign you in, and then keep you signed in.
• For the dictation tool - we use your browser's local storage to store your notes, so you can access them later.
• Non premium dictation tool serves ads by Google. Users may opt out of personalized advertising by visiting Ads Settings . Alternatively, users can opt out of a third-party vendor's use of cookies for personalized advertising by visiting https://youradchoices.com/
• In case you would like to upload files to Google Drive directly from Speechnotes - we'll ask for your permission to do so. We will use that permission for that purpose only - syncing your speech-notes to your Google Drive, per your request.

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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10+ speech data collection services in 2024, top 5 speech recognition data collection methods in 2024, top 4 speech recognition challenges & solutions in 2024.

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Related research

Top 11 Voice Recognition Applications in 2024

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.

Convert speech into text using AI-powered speech recognition and transcription.

Convert text into natural-sounding speech in a variety of languages and voices.

AI-powered hybrid cloud software.

Enable speech transcription in multiple languages for a variety of use cases, including but not limited to customer self-service, agent assistance and speech analytics.

Learn how to keep up, rethink how to use technologies like the cloud, AI and automation to accelerate innovation, and meet the evolving customer expectations.

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How to use speech to text in Microsoft Word

Speech to text in Microsoft Word is a hidden gem that is powerful and easy to use. We show you how to do it in five quick and simple steps

Master the skill of speech to text in Microsoft Word and you'll be dictating documents with ease before you know it. Developed and refined over many years, Microsoft's speech recognition and voice typing technology is an efficient way to get your thoughts out, create drafts and make notes.

Just like the best speech to text apps that make life easier for us when we're using our phones, Microsoft's offering is ideal for those of us who spend a lot of time using Word and don't want to wear out our fingers or the keyboard with all that typing. While speech to text in Microsoft Word used to be prone to errors which you'd then have to go back and correct, the technology has come a long way in recent years and is now amongst the best text-to-speech software .

Regardless of whether you have the best computer or the best Windows laptop , speech to text in Microsoft Word is easy to access and a breeze to use. From connecting your microphone to inserting punctuation, you'll find everything you need to know right here in this guide. Let's take a look...

How to use speech to text in Microsoft Word: Preparation

The most important thing to check is whether you have a valid Microsoft 365 subscription, as voice typing is only available to paying customers. If you’re reading this article, it’s likely your business already has a Microsoft 365 enterprise subscription. If you don’t, however, find out more about Microsoft 365 for business via this link . 

The second thing you’ll need before you start voice typing is a stable internet connection. This is because Microsoft Word’s dictation software processes your speech on external servers. These huge servers and lighting-fast processors use vast amounts of speech data to transcribe your text. In fact, they make use of advanced neural networks and deep learning technology, which enables the software to learn about human speech and continuously improve its accuracy. 

These two technologies are the key reason why voice typing technology has improved so much in recent years, and why you should be happy that Microsoft dictation software requires an internet connection. 

Once you’ve got a valid Microsoft 365 subscription and an internet connection, you’re ready to go!

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Step 1: Open Microsoft Word

Simple but crucial. Open the Microsoft Word application on your device and create a new, blank document. We named our test document “How to use speech to text in Microsoft Word - Test” and saved it to the desktop so we could easily find it later.

Step 2: Click on the Dictate button

Once you’ve created a blank document, you’ll see a Dictate button and drop-down menu on the top right-hand corner of the Home menu. It has a microphone symbol above it. From here, open the drop-down menu and double-check that the language is set to English.

One of the best parts of Microsoft Word’s speech to text software is its support for multiple languages. At the time of writing, nine languages were supported, with several others listed as preview languages. Preview languages have lower accuracy and limited punctuation support.

Step 3: Allow Microsoft Word access to the Microphone

If you haven’t used Microsoft Word’s speech to text software before, you’ll need to grant the application access to your microphone. This can be done at the click of a button when prompted.

It’s worth considering using an external microphone for your dictation, particularly if you plan on regularly using voice to text software within your organization. While built-in microphones will suffice for most general purposes, an external microphone can improve accuracy due to higher quality components and optimized placement of the microphone itself.

Step 4: Begin voice typing

Now we get to the fun stuff. After completing all of the above steps, click once again on the dictate button. The blue symbol will change to white, and a red recording symbol will appear. This means Microsoft Word has begun listening for your voice. If you have your sound turned up, a chime will also indicate that transcription has started. 

Using voice typing is as simple as saying aloud the words you would like Microsoft to transcribe. It might seem a little strange at first, but you’ll soon develop a bit of flow, and everyone finds their strategies and style for getting the most out of the software. 

These four steps alone will allow you to begin transcribing your voice to text. However, if you want to elevate your speech to text software skills, our fifth step is for you.

Step 5: Incorporate punctuation commands

Microsoft Word’s speech to text software goes well beyond simply converting spoken words to text. With the introduction and improvement of artificial neural networks, Microsoft’s voice typing technology listens not only to single words but to the phrase as a whole. This has enabled the company to introduce an extensive list of voice commands that allow you to insert punctuation marks and other formatting effects while speaking. 

We can’t mention all of the punctuation commands here, but we’ll name some of the most useful. Saying the command “period” will insert a period, while the command “comma” will insert, unsurprisingly, a comma. The same rule applies for exclamation marks, colons, and quotations. If you’d like to finish a paragraph and leave a line break, you can say the command “new line.” 

These tools are easy to use. In our testing, the software was consistently accurate in discerning words versus punctuation commands.

Microsoft’s speech to text software is powerful. Having tested most of the major platforms, we can say that Microsoft offers arguably the best product when balancing cost versus performance. This is because the software is built directly into Microsoft 365, which many businesses already use. If this applies to your business, you can begin using Microsoft’s voice typing technology straight away, with no additional costs. 

We hope this article has taught you how to use speech to text software in Microsoft Word, and that you’ll now be able to apply these skills within your organization. 

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Speech Recognition

Save time transforming speech into text with AI-powered speech recognition.

Boost productivity and improve accuracy with AI speech recognition

Effortlessly convert spoken words into accurate and reliable text with automatic speech recognition software. Say goodbye to time-consuming manual transcriptions. Embrace the efficiency and precision of VEED’s AI-powered solution. Focus on the content itself, without the hassle of transcribing word by word. Create content faster with VEED’s AI video editing tools .

With a high level of accuracy, you can trust that every word will be captured. According to recent studies, AI-powered speech recognition systems can achieve an accuracy rate of over 95%, surpassing human transcription in many cases.

How to transcribe videos with the help of speech recognition:

Upload your audio or video file

Select your audio or video file from your folders. You can also drag and drop your file into the box.

Auto transcribe

Click on ‘Subtitles’ from the left menu and select ‘ Auto Subtitles ’. Select a language and click ‘Start’. Make changes to the transcription, if needed.

Download the text file

While on the Subtitles page, click on ‘Options’ then hit the download icon. You’re done! Make sure to select the format that you prefer.

Watch this to learn more about our AI speech recognition software:

High productivity with automated transcriptions

Streamline your workflow and save countless hours by automating the transcription process. VEED's AI Speech Recognition software accurately converts speech into text , allowing you to focus on creating and editing content rather than transcribing. Increase your productivity and free up valuable time for other tasks.

Accurate & reliable transcriptions with AI

Trust in the accuracy and reliability of our AI-powered Speech Recognition. Our advanced algorithms ensure precise transcription of your audio recordings or videos. According to industry reports, AI-based speech recognition systems can achieve word error rates as low as 4%, rivaling human performance.

Versatile applications for content creators

Whether you're a journalist, podcaster, researcher, or content creator, VEED's AI Speech Recognition caters to a wide range of industries and applications. Transcribe interviews, lectures, webinars, and videos with ease. Use the transcriptions for captions, subtitles, content analysis, or documentation. The possibilities are endless.

Frequently Asked Questions

AI Speech Recognition is a technology that utilizes artificial intelligence algorithms to convert spoken words into written text. It allows for automated transcription, eliminating the need for manual transcriptions and increasing efficiency.

VEED's AI Speech Recognition software leverages advanced algorithms to achieve a high level of accuracy in transcribing speech. While accuracy may vary depending on factors such as audio quality and accent, our system strives to provide reliable and precise transcriptions.

Yes, VEED's AI Speech Recognition supports multiple languages and can handle various accents. Our system is designed to recognize and transcribe speech in different languages, making it versatile and accessible for users around the world.

The time taken to transcribe a speech or audio recording depends on its length and complexity. However, VEED's AI Speech Recognition software processes transcriptions efficiently, providing faster results compared to manual transcription methods.

AI Speech Recognition has numerous applications for professionals. It can be used for transcription services, generating captions and subtitles for videos, content creation, market research, and more. It simplifies the process of converting spoken content into written text, facilitating better accessibility and workflow optimization.

What they say about VEED

Veed is a great piece of browser software with the best team I've ever seen. Veed allows for subtitling, editing, effect/text encoding, and many more advanced features that other editors just can't compete with. The free version is wonderful, but the Pro version is beyond perfect. Keep in mind that this a browser editor we're talking about and the level of quality that Veed allows is stunning and a complete game changer at worst.

I love using VEED as the speech to subtitles transcription is the most accurate I've seen on the market. It has enabled me to edit my videos in just a few minutes and bring my video content to the next level

Laura Haleydt - Brand Marketing Manager, Carlsberg Importers

The Best & Most Easy to Use Simple Video Editing Software! I had tried tons of other online editors on the market and been disappointed. With VEED I haven't experienced any issues with the videos I create on there. It has everything I need in one place such as the progress bar for my 1-minute clips, auto transcriptions for all my video content, and custom fonts for consistency in my visual branding.

Diana B - Social Media Strategist, Self Employed

AI tools to make video editing easier!

VEED’s magic doesn’t just stop at AI speech recognition and transcription. It’s a professional, all-in-one video editing suite that features all the tools you need to create amazing-looking videos—always in pro quality! Share stories only you can tell through videos that go beyond what’s expected.

Add images , music and much more. All online; no software to download. Try it now, and start creating content that pushes your creative boundaries!

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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Type with your Voice in any language

Use the magic of speech recognition to write emails and documents in Google Chrome.

Dictation accurately transcribes your speech to text in real time. You can add paragraphs, punctuation marks, and even smileys using voice commands.

Voice Dictation - Type with your Voice

Dictation can recognize and transcribe popular languages including English, Español, Français, Italiano, Português, हिन्दी, தமிழ், اُردُو, বাংলা, ગુજરાતી, ಕನ್ನಡ, and more. See full list of supported languages .

You can add new paragraphs, punctuation marks, smileys and other special characters using simple voice commands. For instance, say "New line" to move the cursor to the next list or say "Smiling Face" to insert :-) smiley. See list of supported voice commands .

Dictation uses Google Speech Recognition to transcribe your spoken words into text. It stores the converted text in your browser locally and no data is uploaded anywhere. Learn more .

Speech to Text

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System Requirements

Google Chrome Windows/Mac/Linux Internet Connection

Voice Commands Dictation FAQ Contact Support

What is Speech Recognition?

Rev › Blog › Speech to Text Technology › What is Speech Recognition?

Speech recognition is when a machine or computer program identifies and processes a person’s spoken words and converts them into text displayed on a screen or monitor. The early stages of this technology utilized a limited vocabulary set that included common phrases and words.

As the software and technology has evolved, it is now able to more accurately interpret natural speech as well as identify differences between accents and different languages. While speech recognition has come a long way, there is still much room for improvement.

The terms speech recognition and voice recognition are often used to refer to the same thing. However, the two are different. Speech recognition is used to identify the words someone has spoken. Voice recognition is a biometric technology used to identify a specific person’s voice.

Speech recognition can be used to perform a voice search whereas voice recognition can be used by a doctor to dictate medical transcription reports. If you have ever had to call your internet service provider for assistance, you may recall having to go through a series of voice-activated prompts. The call center uses speech recognition technology to route you to the right department. 

Why use speech recognition?

So why would someone need speech recognition? Today, practically everyone owns and operates smart devices, such as cell phones and digital tablets. Speech recognition technology has become one of many features hard-coded into the software of these smart devices, allowing them to comprehend continuous speech and translate it into different actions.

For example, a user can verbally tell their mobile device to “call Mom”, and the device acknowledges the command and performs the desired action in real-time. Another use case is using a digital assistant like Google or Siri to initiate a voice search.

Some other ways people use speech recognition is to play their music hands-free, print documents, record audio, get updates on weather conditions, make travel arrangements, find cooking recipes, and much more. 

How does it work?

At this point, you may be thinking that speech recognition is pretty great but how does it actually work? Computers and other devices are equipped with built-in external microphones and other sensors that pick up the words a person may speak, and these components translate the sound waves of a voice into digital information the device can use. Many different computer programs are used to interpret speech. 

Speech recognition software interprets the sound spoken by a person, which is then analyzed and sampled to remove any background noise. It then separates the digital information into separate frequencies. The software takes this information and attempts to examine and compare the fundamentals with other fundamentals to an extensive library of words, expressions, and sentences. The software then determines what the person said and provides the text output or performs the command.

It is also worth understanding the word error rate or ( WER ). Word error rate is calculated by the number of errors divided by the number of total words processed. More specifically, a simple formula used to calculate this rate is as follows: Substitutions + Insertions + Deletions divided by the Total Number of words spoken. This calculation was derived from something called the “Levenshtein distance” which involves measuring the distance between two  strings . In this scenario, a string can be considered a sequence of letters that form the words within a transcription.

When choosing a speech recognition software, look for low WER scores. The lower the WER score, the more closely it is that the transcript matches the audio. For example, Rev’s speech recognition product has a 14% WER, or an 86% accuracy rate, which beats Google, Amazon, Microsoft, and other major speech-to-text options .

As speech recognition plays an increasingly greater role in our lives, it’s important to understand how it works. If you are looking for your own speech-to-text services, consider the quality of the service you choose. Rev’s leading speech-to-text A.I. and its community of freelance professionals offer quick and affordable speech-to-text services with 99 percent accuracy. 

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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

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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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How to set up and use Windows 10 Speech Recognition

Windows 10 has a hands-free using Speech Recognition feature, and in this guide, we show you how to set up the experience and perform common tasks.

On Windows 10 , Speech Recognition is an easy-to-use experience that allows you to control your computer entirely with voice commands.

Anyone can set up and use this feature to navigate, launch applications, dictate text, and perform a slew of other tasks. However, Speech Recognition was primarily designed to help people with disabilities who can't use a mouse or keyboard.

In this Windows 10 guide, we walk you through the steps to configure and start using Speech Recognition to control your computer only with voice.

How to configure Speech Recognition on Windows 10

How to train speech recognition to improve accuracy, how to change speech recognition settings, how to use speech recognition on windows 10.

To set up Speech Recognition on your device, use these steps:

• Open Control Panel .
• Click on Ease of Access .
• Click on Speech Recognition .
• Click the Start Speech Recognition link.
• In the "Set up Speech Recognition" page, click Next .
• Select the type of microphone you'll be using. Note: Desktop microphones are not ideal, and Microsoft recommends headset microphones or microphone arrays.
• Click Next .
• Click Next again.
• Read the text aloud to ensure the feature can hear you.
• Speech Recognition can access your documents and emails to improve its accuracy based on the words you use. Select the Enable document review option, or select Disable document review if you have privacy concerns.
• Use manual activation mode — Speech Recognition turns off the "Stop Listening" command. To turn it back on, you'll need to click the microphone button or use the Ctrl + Windows key shortcut.
• Use voice activation mode — Speech Recognition goes into sleep mode when not in use, and you'll need to invoke the "Start Listening" voice command to turn it back on.
• If you're not familiar with the commands, click the View Reference Sheet button to learn more about the voice commands you can use.
• Select whether you want this feature to start automatically at startup.
• Click the Start tutorial button to access the Microsoft video tutorial about this feature, or click the Skip tutorial button to complete the setup.

Once you complete these steps, you can start using the feature with voice commands, and the controls will appear at the top of the screen.

Quick Tip: You can drag and dock the Speech Recognition interface anywhere on the screen.

After the initial setup, we recommend training Speech Recognition to improve its accuracy and to prevent the "What was that?" message as much as possible.

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• Click the Train your computer to better understand you link.
• Click Next to continue with the training as directed by the application.

After completing the training, Speech Recognition should have a better understanding of your voice to provide an improved experience.

If you need to change the Speech Recognition settings, use these steps:

• Click the Advanced speech options link in the left pane.

Inside "Speech Properties," in the Speech Recognition tab, you can customize various aspects of the experience, including:

• Recognition profiles.
• User settings.
• Microphone.

In the Text to Speech tab, you can control voice settings, including:

• Voice selection.
• Voice speed.

Additionally, you can always right-click the experience interface to open a context menu to access all the different features and settings you can use with Speech Recognition.

While there is a small learning curve, Speech Recognition uses clear and easy-to-remember commands. For example, using the "Start" command opens the Start menu, while saying "Show Desktop" will minimize everything on the screen.

If Speech Recognition is having difficulties understanding your voice, you can always use the Show numbers command as everything on the screen has a number. Then say the number and speak OK to execute the command.

Here are some common tasks that will get you started with Speech Recognition:

Starting Speech Recognition

To launch the experience, just open the Start menu , search for Windows Speech Recognition , and select the top result.

Turning on and off

To start using the feature, click the microphone button or say Start listening depending on your configuration.

In the same way, you can turn it off by saying Stop listening or clicking the microphone button.

Using commands

Some of the most frequent commands you'll use include:

• Open — Launches an app when saying "Open" followed by the name of the app. For example, "Open Mail," or "Open Firefox."
• Switch to — Jumps to another running app when saying "Switch to" followed by the name of the app. For example, "Switch to Microsoft Edge."
• Control window in focus — You can use the commands "Minimize," "Maximize," and "Restore" to control an active window.
• Scroll — Allows you to scroll in a page. Simply use the command "Scroll down" or "Scroll up," "Scroll left" or "Scroll right." It's also possible to specify long scrolls. For example, you can try: "Scroll down two pages."
• Close app — Terminates an application by saying "Close" followed by the name of the running application. For example, "Close Word."
• Clicks — Inside an application, you can use the "Click" command followed by the name of the element to perform a click. For example, in Word, you can say "Click Layout," and Speech Recognition will open the Layout tab. In the same way, you can use "Double-click" or "Right-click" commands to perform those actions.
• Press — This command lets you execute shortcuts. For example, you can say "Press Windows A" to open Action Center.

Using dictation

Speech Recognition also includes the ability to convert voice into text using the dictation functionality, and it works automatically.

If you need to dictate text, open the application (making sure the feature is in listening mode) and start dictating. However, remember that you'll have to say each punctuation mark and special character.

For example, if you want to insert the "Good morning, where do you like to go today?" sentence, you'll need to speak, "Open quote good morning comma where do you like to go today question mark close quote."

In the case that you need to correct some text that wasn't recognized accurately, use the "Correct" command followed by the text you want to change. For example, if you meant to write "suite" and the feature recognized it as "suit," you can say "Correct suit," select the suggestion using the correction panel or say "Spell it" to speak the correct text, and then say "OK".

Wrapping things up

Although Speech Recognition doesn't offer a conversational experience like a personal assistant, it's still a powerful tool for anyone who needs to control their device entirely using only voice.

Cortana also provides the ability to control a device with voice, but it's limited to a specific set of input commands, and it's not possible to control everything that appears on the screen.

However, that doesn't mean that you can't get the best of both worlds. Speech Recognition runs independently of Cortana, which means that you can use the Microsoft's digital assistant for certain tasks and Speech Recognition to navigate and execute other commands.

It's worth noting that this speech recognition isn't available in every language. Supported languages include English (U.S. and UK), French, German, Japanese, Mandarin (Chinese Simplified and Chinese Traditional), and Spanish.

While this guide is focused on Windows 10, Speech Recognition has been around for a long time, so you can refer to it even if you're using Windows 8.1 or Windows 7.

More Windows 10 resources

For more helpful articles, coverage, and answers to common questions about Windows 10, visit the following resources:

• Windows 10 on Windows Central – All you need to know
• Windows 10 help, tips, and tricks
• Windows 10 forums on Windows Central

Mauro Huculak is technical writer for WindowsCentral.com. His primary focus is to write comprehensive how-tos to help users get the most out of Windows 10 and its many related technologies. He has an IT background with professional certifications from Microsoft, Cisco, and CompTIA, and he's a recognized member of the Microsoft MVP community.

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AI + Machine Learning , Announcements , Azure AI , Azure AI Speech , Azure OpenAI Service , Speech to text

Accelerate your productivity with the Whisper model in Azure AI now generally available

By Marco Casalaina Vice President Of Products, Azure AI

Posted on March 13, 2024 4 min read

Human speech remains one of the most complex things for computers to process. With thousands of spoken languages in the world, enterprises often struggle to choose the right technologies to understand and analyze audio conversations while keeping right data security and privacy guardrails in place. Thanks to generative AI, it has become easier for enterprises to analyze every customer interaction and derive actionable insights from these interactions.

Build intelligent apps at enterprise scale with the Azure AI portfolio.

Azure AI offers an industry-leading portfolio of AI services to help customers make sense of their voice data. Our speech-to-text service in particular offers a variety of differentiated features through Azure OpenAI Service and Azure AI Speech. These features have been instrumental in helping customers develop multilingual speech transcription and translation, both for long audio files and for near-real-time and real-time assistance for customer service representatives.

Today, we are excited to announce that OpenAI Whisper on Azure is generally available. Whisper is a speech to text model from OpenAI that developers can use to transcribe audio files. Starting today, developers can begin using the generally available Whisper API in both Azure OpenAI Service as well as Azure AI Speech services on production workloads, knowing that it is backed by Azure’s enterprise-readiness promise. With all our speech-to-text models generally available, customers have greater choice and flexibility to enable AI powered transcription and other speech scenarios.

Since the public preview of the Whisper API in Azure , thousands of customers across industries across healthcare, education, finance, manufacturing, media, agriculture, and more are using it to translate and transcribe audio into text across many of the 57 supported languages. They use Whisper to process call center conversations, add captions for accessibility purposes to audio and video content, and mine audio and video data for actionable insights. 

We continue to bring OpenAI models to Azure to enrich our portfolio and address the next generation of use-cases and workflows customers are looking to build with speech technologies and LLMs. For instance, imagine building an end-to-end contact center workflow—with a self-service copilot carrying out human-like conversations with end users through voice or text; an automated call routing solution; real-time agent assistance copilots; and automated post-call analytics. This end-to-end workflow, powered by generative AI, has the potential to bring a new era in productivity to call centers around the world.

Whisper in Azure OpenAI Service  

Azure OpenAI Service enables developers to run OpenAI’s Whisper model in Azure, mirroring the OpenAI Whisper model functionalities including fast processing time, multi-lingual support, and transcription and translation capabilities. OpenAI Whisper in Azure OpenAI Service is ideal for processing smaller size files for time-sensitive workloads and use-cases. 

Lightbulb.ai , an AI innovator, is looking to transform call center workflows, has been using Whisper in Azure OpenAI Service.

“By merging our call center expertise with tools like Whisper and a combination of LLMs, our product is proven to be 500X more scalable, 90X faster, and 20X more cost-effective than manual call reviews and enables third-party administrators, brokerages, and insurance companies to not only eliminate compliance risk; but also to significantly improve service and boost revenue. We are grateful for our partnership with Azure, which has been instrumental in our success, and we’re enthusiastic about continuing to leverage Whisper to create unprecedented outcomes for our customers.” Tyler Amundsen, CEO and Co-Founder, Lightbulb.AI

To learn more about how to use the Whisper model with the Azure OpenAI Service click here:  Speech to text with Azure OpenAI Service . 

Try out the Whisper REST (representational state transfer) API in the Azure OpenAI Studio . The API supports translation services from a growing list of languages to English, producing English-only output. 

OpenAI Whisper model in Azure AI Speech 

Users of Azure AI Speech can leverage OpenAI’s Whisper model in conjunction with the Azure AI Speech batch transcription API. This enables customers to easily transcribe large volumes of audio content at scale for non-time-sensitive batch workloads.

Developers using Whisper in Azure AI Speech also benefit from the following additional capabilities:

• Processing of large file sizes up to 1GB in size with the ability to process large amounts of files with up to 1000 files in a single request that processes multiple audio files simultaneously.
• Speaker diarization which allows developers to distinguish between different speakers, accurately transcribe their words, and create a more organized and structured transcription of audio files.
• And lastly, developers can use Custom Speech in Speech Studio or via API to finetune the Whisper model using audio plus human labeled transcripts. 

Customers are using Whisper in Azure AI Speech for post-call analysis, deriving insights from audio and video recordings, and many more such applications. 

For details on how to use the Whisper model with Azure AI Speech click here:  Create a batch transcription .

Getting started with Whisper

Azure openai studio  .

Developers preferring to use the Whisper model in Azure OpenAI Service can access it through the  Azure OpenAI Studio.  

• To gain access to Azure OpenAI Service, users need to  apply for access . 
• Once approved, visit the  Azure portal  and create an Azure OpenAI Service resource. 
• After creating the resource, users can begin using Whisper. 

Azure AI Speech Studio  

Developers preferring to use the Whisper model in Azure AI Speech can access it through the batch speech-to-text in Azure AI Speech Studio.    

The batch speech to text try-out allows you to compare the output of the Whisper model side by side with an Azure AI Speech model as a quick initial evaluation of which model may work better for your specific scenario. 

The Whisper model is a great addition to the broad portfolio of capabilities that Azure AI offers. We are looking forward to seeing the innovative ways in which developers will take advantage of this new offering to improve business productivity and to delight users. 

Let us know what you think of Azure and what you would like to see in the future.

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Electrical Engineering and Systems Science > Audio and Speech Processing

Title: banglanum -- a public dataset for bengali digit recognition from speech.

Abstract: Automatic speech recognition (ASR) converts the human voice into readily understandable and categorized text or words. Although Bengali is one of the most widely spoken languages in the world, there have been very few studies on Bengali ASR, particularly on Bangladeshi-accented Bengali. In this study, audio recordings of spoken digits (0-9) from university students were used to create a Bengali speech digits dataset that may be employed to train artificial neural networks for voice-based digital input systems. This paper also compares the Bengali digit recognition accuracy of several Convolutional Neural Networks (CNNs) using spectrograms and shows that a test accuracy of 98.23% is achievable using parameter-efficient models such as SqueezeNet on our dataset.

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Microsoft to replace Windows Speech Recognition with Voice Access

Microsoft is sunsetting Windows Speech Recognition (WSR) and replacing it with Voice Access. The change has been on the cards since last year, but the company has now indicated the timeline. Microsoft has announced that it will transition Windows 11’s speech recognition to the new Voice Access platform later this year. The new system is better than WSR, however, Windows 10 users will have to make some tough choices.

Only Microsoft Windows 11 22H2 and later versions get Voice Access

Microsoft has been steadily scaling back access to Windows Speech Recognition. However, the company’s motives weren’t clear, until this week. Microsoft has confirmed that the new Voice Access app will entirely replace the WSR app. The change will take place in Windows 11 22H2 and newer versions . The WSR app should cease to be available in September 2024, according to  a new Support Document :

“Windows 11 22H2 and later, Windows Speech Recognition (WSR) will be replaced by voice access starting in September 2024.” Microsoft has made it clear that it will keep WSR operational on Windows 11 21H2. What this also means is that Windows 10 users will have to continue relying on the deprecated platform.

Incidentally, the Voice Access app is exclusive to Windows 11. Hence, Windows 10 loyalists will have to decide to upgrade to Windows 11 if they wish to use Voice Access. Windows 10 will reach its end of support on October 14, 2025. In other words, Windows 10 users do not have much time to stick to the OS before upgrading to Windows 11.

Why Is Microsoft Retiring WSR?

Microsoft has been prioritizing Voice Access over WSR for quite some time. While Voice Access and WSR appear on the same Accessibility settings page inside the Windows 11 Settings app, the latter appears under the ‘Other voice commands’ section. Microsoft has been warning WSR users that support for the platform is ending. As the company has already confirmed the deprecation of WSR, the platform won’t be getting any new features or updates.

Currently, WSR has an edge over Voice Access because it supports far more languages. However, that’s where its superiority ends. WSR has always had trouble understanding the English language and the simplest of phrases or commands. Several users reported they turned WSR off after unsuccessfully using it to compose emails.

Voice Access, on the other hand, is backed by AI, which is increasingly evolving into Artificial General Intelligence (AGI). AI has a far better understanding of how humans interact and understand each other. Additionally, Microsoft has been actively adding languages to Voice Access. Besides supporting regional dialects of English, Voice Access now supports French German, and Spanish from multiple locales.

While Microsoft will continue improving Voice Access, the company is also integrating Copilot deeper inside Windows 11. When used together, Windows 11 users would gradually be able to control or change OS settings without ever opening the Settings app.

The post Microsoft to replace Windows Speech Recognition with Voice Access appeared first on Android Headlines .

Slowed speech may indicate cognitive decline more accurately than forgetting words

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Disclosure statement

Claire Lancaster receives funding from the Economic and Social Research Council and Sussex Partnership NHS Foundation Trust to investigate speech-based markers of neurodegenerative disease.

Alice Stanton receives funding from the Economic and Social Research Council and Sussex Partnership NHS Foundation Trust to investigate speech-based markers of neurodegenerative disease.

University of Sussex provides funding as a member of The Conversation UK.

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Can you pass me the whatchamacallit? It’s right over there next to the thingamajig.

Many of us will experience “lethologica”, or difficulty finding words, in everyday life. And it usually becomes more prominent with age.

Frequent difficulty finding the right word can signal changes in the brain consistent with the early (“preclinical”) stages of Alzheimer’s disease – before more obvious symptoms emerge. However, a recent study from the University of Toronto suggests that it’s the speed of speech, rather than the difficulty in finding words that is a more accurate indicator of brain health in older adults.

The researchers asked 125 healthy adults, aged 18 to 90, to describe a scene in detail. Recordings of these descriptions were subsequently analysed by artificial intelligence (AI) software to extract features such as speed of talking, duration of pauses between words, and the variety of words used.

Participants also completed a standard set of tests that measure concentration, thinking speed, and the ability to plan and carry out tasks. Age-related decline in these “executive” abilities was closely linked to the pace of a person’s everyday speech, suggesting a broader decline than just difficulty in finding the right word.

A novel aspect of this study was the use of a “picture-word interference task”, a clever task designed to separate the two steps of naming an object: finding the right word and instructing the mouth on how to say it out loud.

During this task, participants were shown pictures of everyday objects (such as a broom) while being played an audio clip of a word that is either related in meaning (such as “mop” – which makes it harder to think of the picture’s name) or which sounds similar (such as “groom” – which can make it easier).

Interestingly, the study found that the natural speech speed of older adults was related to their quickness in naming pictures. This highlights that a general slowdown in processing might underlie broader cognitive and linguistic changes with age, rather than a specific challenge in memory retrieval for words.

How to make the findings more powerful

While the findings from this study are interesting, finding words in response to picture-based cues may not reflect the complexity of vocabulary in unconstrained everyday conversation.

Verbal fluency tasks, which require participants to generate as many words as possible from a given category (for example, animals or fruits) or starting with a specific letter within a time limit, may be used with picture-naming to better capture the “tip-of-the-tongue” phenomenon.

The tip-of-the-tongue phenomenon refers to the temporary inability to retrieve a word from memory, despite partial recall and the feeling that the word is known. These tasks are considered a better test of everyday conversations than the picture-word interference task because they involve the active retrieval and production of words from one’s vocabulary, similar to the processes involved in natural speech.

While verbal fluency performance does not significantly decline with normal ageing (as shown in a 2022 study ), poor performance on these tasks can indicate neurodegenerative diseases such as Alzheimer’s.

The tests are useful because they account for the typical changes in word retrieval ability as people get older, allowing doctors to identify impairments beyond what is expected from normal ageing and potentially detect neurodegenerative conditions.

The verbal fluency test engages various brain regions involved in language, memory, and executive functioning, and hence can offer insights into which regions of the brain are affected by cognitive decline.

The authors of the University of Toronto study could have investigated participants’ subjective experiences of word-finding difficulties alongside objective measures like speech pauses. This would provide a more comprehensive understanding of the cognitive processes involved.

Personal reports of the “feeling” of struggling to retrieve words could offer valuable insights complementing the behavioural data, potentially leading to more powerful tools for quantifying and detecting early cognitive decline.

Read more: Daily fibre supplement improves older adults’ brain function in just three months – new study

Opening doors

Nevertheless, this study has opened exciting doors for future research, showing that it’s not just what we say but how fast we say it that can reveal cognitive changes.

By harnessing natural language processing technologies (a type of AI), which use computational techniques to analyse and understand human language data, this work advances previous studies that noticed subtle changes in the spoken and written language of public figures like Ronald Reagan and Iris Murdoch in the years before their dementia diagnoses.

While those opportunistic reports were based on looking back after a dementia diagnosis, this study provides a more systematic, data-driven and forward-looking approach.

Using rapid advancements in natural language processing will allow for automatic, detection of language changes, such as slowed speech rate.

This study underscores the potential of speech rate changes as a significant yet subtle marker of cognitive health that could aid in identifying people at risk before more severe symptoms become apparent.

Read more: Could many dementia cases actually be liver disease?

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Windows Speech Recognition commands

On Windows 11 22H2 and later, Windows Speech Recognition (WSR) will be replaced by voice access starting in September 2024. Older versions of Windows will continue to have WSR available. To learn more about voice access, go to Use voice access to control your PC & author text with your voice .

Windows Speech Recognition lets you control your PC by voice alone, without needing a keyboard or mouse. This article lists commands that you can use with Speech Recognition.

For instructions on how to set up Speech Recognition for the first time, refer to  Use voice recognition in Windows .

Any time you need to find out what commands to use, say "What can I say?"

Speech Recognition is available only for the following languages: English (United States, United Kingdom, Canada, India, and Australia), French, German, Japanese, Mandarin (Chinese Simplified and Chinese Traditional), and Spanish.

In the following tables, a bolded word or phrase means it's an example. Replace it with similar words to get the result you want.

In this topic

Common speech recognition commands, commands for dictation, commands for the keyboard, commands for punctuation marks and special characters.

Commands for Windows and apps

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You can also use the ICAONATO phonetic alphabet. For example, say "press alpha" to press A or "press bravo" to press B.

Speech Recognition commands for the keyboard works only with languages that use Latin alphabets.

Commands for Windows and apps

Use voice recognition in Windows  

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