Speech recognition is classified into two categories, speaker dependent and speaker independent. Speaker dependent systems are trained by the individual who will be using the system. These systems are capable of achieving a high command count and better than 95% accuracy for word recognition. SimpleVR is a speaker-independent voice recognition module designed to add versatile, robust and cost effective speech and voice recognition capabilities to almost any application. Different from another voice recognition module Speak Recognition, Voice Recognition Module V3, SimpleVR is speaker-independent. The LPC54114 Audio and Voice Recognition Kit provides a complete hardware and software platform for developers to evaluate and prototype with the LPC54114 processor family. It has been developed by NXP ® to provide all that you need to develop an always-on low power voice triggering product.
Simplevr Speaker Independent Voice Recognition Module Program For Mac 2017
Latest version Released:
a speech recognition system structured based on an acoustic and a language model
Project description
Speech Recognition wave2words
This package is a test sample and contains two functions and acts as a single program:
a pronunciation/rhythm/stress words-phrases game (English). The package could be customized for
a speech-to-text system by accepting input from a microphone or an audio file or both. The
package could be structured for any language of choice.
In this package, we will test our wave2word speech recognition using AI, for English. However,
in the future releases, other languages will be added to make a language-independent speech
recognition.
Concept
We may guess a word from unknown spoken languages just by listening to the sound of the Speech.
To us, in an interaction between a human and a machine, the machine should recognise sounds before
making sense of any words (built from the combination of sounds) meaning. In other words,
without pre-determining the language a speech recognition should pick up sounds and form words.
Our speech processing system focuses on the process of understanding the acoustic features of
sounds, then building words that are spoken by human beings. The speech signals are captured with
the help of a microphone and then they are to be understood by the system.
The difficulty of speech recognition technology can be broadly characterised along some
dimensions such as 1)size of a specific language vocabulary pool, 2)speaker dependency-sounds of
a particular word vary from a person to another, 3) the importance of channel quality; human speech
contains high bandwidth with full frequency range, while a telephone speech consists of low
bandwidth with limited frequency range, 4)speaking mode that is whether the speech is in isolated
word mode, or connected word mode, or in a continuous speech mode. A continuous speech is
harder to recognize, 5)speaking style; a loud-read speech, spontaneous and conversational, 6) type
of the noise − signal to noise ratio may be in various ranges, depending on the acoustic environment
that observes less versus more background noise, 7) microphone quality and the distance between mouth
and microphone.
Recording and Sampling
During recording with a microphone, the signals are stored in a digitised form. But to work upon it,
the machine needs them in the discrete numeric form. Hence, our algorithm should sample the signals
at a particular frequency and convert the signal into the discrete numerical form. Choosing the high
frequency for sampling implies that when humans listen to the signal, they feel it as a continuous
audio signal.
Transforming to Frequency Domain
Characterising an audio signal involves converting the time domain signal into the frequency domain,
and understanding its frequency components that is an essential step because it gives a lot of information
about the signal. You can use a mathematical tool like Fourier Transform to perform this transformation.
This transformation is the most critical step in building a speech recogniser because after converting the
speech signal into the frequency domain, we must convert it into the usable form of the feature vector.
We can use different feature extraction techniques like MFCC, PLP, PLP-RASTA etc. for this purpose.
Myvoicerecognition is unique in its aim to provide a complete quantitative and analytical way to study the acoustic
features of a speech. Moreover, those features could be analysed further by employing Python's functionality
to provide more fascinating insights into speech patterns.
This library is for Linguists, scientists, developers, speech and language therapy clinics and researchers.
Please note that Myvoicerecognition Analysis is currently in the initial state though in active development. While
the amount of functionality that is now present is not huge; more will be added over the next few months.
Installation
Myvoicerecognition can be installed like any other Python library, using (a recent version of) the Python package
manager pip, on Linux, macOS, and Windows:
------------- pip install Myvoicerecognition ------------------------------
or, to update your installed version to the latest release:
------------- pip install -u Myvoicerecognition ---------------------------------
NOTE:
You need to get the following packages installed:
-----the Microsoft Visual C++ Redistributable for Visual Studio 2017 ------x86 or x64-----see your system
-----PyAudio---PyAudio>= 0.2.11---pip install PyAudio (win),
----------------------------------$ sudo apt-get install python-pyaudio python3-pyaudio (Debian-based Linux
----------------------------------$ brew install portaudio ----$ pip install pyaudio (MaC)
-----PyAudio-0.2.11-cp37-cp37m-win32.whl or win64.whl -----if your system throws an error for PyAudio
you may get the third file from
---------- https://github.com/Shahabks/Myvoicerecognition------
save it in a directory and in cmd (command line)../directory/ pip install PyAudio-0.2.11-cp37-cp37m-winxx.whl.
The package uses the default system microphone. If your system has no default microphone, or you want to use
a microphone other than the default, you will need to specify which one to use by supplying a device index.
To check how the Myvoicerecognition functions behave, please check
---------------- EXAMPLES.docx on --------
------------- https://github.com/Shahabks/Myvoicerecognition-----
Myvoicerecognition was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of Voice
Recognition and Acoustic & Language modeling Project in MYSOLUTIONS Lab. That is planned to enrich
the functionality of Myvoicerecognition by adding more advanced functions.
This package is a test sample and contains two functions and acts as a single program:
a pronunciation/rhythm/stress words-phrases game (English). The package could be customized for
a speech-to-text system by accepting input from a microphone or an audio file or both. The
package could be structured for any language of choice.
In this package, we will test our wave2word speech recognition using AI, for English. However,
in the future releases, other languages will be added to make a language-independent speech
recognition.
Concept
We may guess a word from unknown spoken languages just by listening to the sound of the Speech.
To us, in an interaction between a human and a machine, the machine should recognise sounds before
making sense of any words (built from the combination of sounds) meaning. In other words,
without pre-determining the language a speech recognition should pick up sounds and form words.
Our speech processing system focuses on the process of understanding the acoustic features of
sounds, then building words that are spoken by human beings. The speech signals are captured with
the help of a microphone and then they are to be understood by the system.
The difficulty of speech recognition technology can be broadly characterised along some
dimensions such as 1)size of a specific language vocabulary pool, 2)speaker dependency-sounds of
a particular word vary from a person to another, 3) the importance of channel quality; human speech
contains high bandwidth with full frequency range, while a telephone speech consists of low
bandwidth with limited frequency range, 4)speaking mode that is whether the speech is in isolated
word mode, or connected word mode, or in a continuous speech mode. A continuous speech is
harder to recognize, 5)speaking style; a loud-read speech, spontaneous and conversational, 6) type
of the noise − signal to noise ratio may be in various ranges, depending on the acoustic environment
that observes less versus more background noise, 7) microphone quality and the distance between mouth
and microphone.
Recording and Sampling
During recording with a microphone, the signals are stored in a digitised form. But to work upon it,
the machine needs them in the discrete numeric form. Hence, our algorithm should sample the signals
at a particular frequency and convert the signal into the discrete numerical form. Choosing the high
frequency for sampling implies that when humans listen to the signal, they feel it as a continuous
audio signal.
Transforming to Frequency Domain
Characterising an audio signal involves converting the time domain signal into the frequency domain,
and understanding its frequency components that is an essential step because it gives a lot of information
about the signal. You can use a mathematical tool like Fourier Transform to perform this transformation.
This transformation is the most critical step in building a speech recogniser because after converting the
speech signal into the frequency domain, we must convert it into the usable form of the feature vector.
We can use different feature extraction techniques like MFCC, PLP, PLP-RASTA etc. for this purpose.
Myvoicerecognition is unique in its aim to provide a complete quantitative and analytical way to study the acoustic
features of a speech. Moreover, those features could be analysed further by employing Python's functionality
to provide more fascinating insights into speech patterns.
This library is for Linguists, scientists, developers, speech and language therapy clinics and researchers.
Please note that Myvoicerecognition Analysis is currently in the initial state though in active development. While
the amount of functionality that is now present is not huge; more will be added over the next few months.
Installation
Myvoicerecognition can be installed like any other Python library, using (a recent version of) the Python package
manager pip, on Linux, macOS, and Windows:
------------- pip install Myvoicerecognition ------------------------------
or, to update your installed version to the latest release:
------------- pip install -u Myvoicerecognition ---------------------------------
NOTE:
You need to get the following packages installed:
-----the Microsoft Visual C++ Redistributable for Visual Studio 2017 ------x86 or x64-----see your system
-----PyAudio---PyAudio>= 0.2.11---pip install PyAudio (win),
----------------------------------$ sudo apt-get install python-pyaudio python3-pyaudio (Debian-based Linux
----------------------------------$ brew install portaudio ----$ pip install pyaudio (MaC)
-----PyAudio-0.2.11-cp37-cp37m-win32.whl or win64.whl -----if your system throws an error for PyAudio
you may get the third file from
---------- https://github.com/Shahabks/Myvoicerecognition------
save it in a directory and in cmd (command line)../directory/ pip install PyAudio-0.2.11-cp37-cp37m-winxx.whl.
The package uses the default system microphone. If your system has no default microphone, or you want to use
a microphone other than the default, you will need to specify which one to use by supplying a device index.
To check how the Myvoicerecognition functions behave, please check
---------------- EXAMPLES.docx on --------
------------- https://github.com/Shahabks/Myvoicerecognition-----
Myvoicerecognition was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of Voice
Recognition and Acoustic & Language modeling Project in MYSOLUTIONS Lab. That is planned to enrich
the functionality of Myvoicerecognition by adding more advanced functions.
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macOS Catalina introduces Voice Control, a new way to fully control your Mac entirely with your voice. Voice Control uses the Siri speech-recognition engine to improve on the Enhanced Dictation feature available in earlier versions of macOS.1
How to turn on Voice Control
After upgrading to macOS Catalina, follow these steps to turn on Voice Control:
- Choose Apple menu > System Preferences, then click Accessibility.
- Click Voice Control in the sidebar.
- Select Enable Voice Control. When you turn on Voice Control for the first time, your Mac completes a one-time download from Apple.2
Voice Control preferences
When Voice Control is enabled, you see an onscreen microphone representing the mic selected in Voice Control preferences.
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To pause Voice Control and stop it from from listening, say ”Go to sleep” or click Sleep. To resume Voice Control, say or click ”Wake up.”
How to use Voice Control
Get to know Voice Control by reviewing the list of voice commands available to you: Say “Show commands” or ”Show me what I can say.” The list varies based on context, and you may discover variations not listed. To make it easier to know whether Voice Control heard your phrase as a command, you can select ”Play sound when command is recognized” in Voice Control preferences.
Basic navigation
Voice Control recognizes the names of many apps, labels, controls, and other onscreen items, so you can navigate by combining those names with certain commands. Here are some examples:
- Open Pages: ”Open Pages.” Then create a new document: ”Click New Document.” Then choose one of the letter templates: 'Click Letter. Click Classic Letter.” Then save your document: ”Save document.”
- Start a new message in Mail: ”Click New Message.” Then address it: ”John Appleseed.”
- Turn on Dark Mode: ”Open System Preferences. Click General. Click Dark.” Then quit System Preferences: ”Quit System Preferences” or ”Close window.”
- Restart your Mac: ”Click Apple menu. Click Restart” (or use the number overlay and say ”Click 8”).
You can also create your own voice commands.
Number overlays
Use number overlays to quickly interact with parts of the screen that Voice Control recognizes as clickable, such as menus, checkboxes, and buttons. To turn on number overlays, say ”Show numbers.” Then just say a number to click it.
Number overlays make it easy to interact with complex interfaces, such as web pages. For example, in your web browser you could say ”Search for Apple stores near me.” Then use the number overlay to choose one of the results: ”Show numbers. Click 64.” (If the name of the link is unique, you might also be able to click it without overlays by saying ”Click” and the name of the link.)
Voice Control automatically shows numbers in menus and wherever you need to distinguish between items that have the same name.
Grid overlays
Use grid overlays to interact with parts of the screen that don't have a control, or that Voice Control doesn't recognize as clickable.
Say “Show grid” to show a numbered grid on your screen, or ”Show window grid” to limit the grid to the active window. Say a grid number to subdivide that area of the grid, and repeat as needed to continue refining your selection.
To click the item behind a grid number, say ”Click” and the number. Or say ”Zoom” and the number to zoom in on that area of the grid, then automatically hide the grid. You can also use grid numbers to drag a selected item from one area of the grid to another: ”Drag 3 to 14.”
To hide grid numbers, say ”Hide numbers.” To hide both numbers and grid, say ”Hide grid.”
Dictation
When the cursor is in a document, email message, text message, or other text field, you can dictate continuously. Dictation converts your spoken words into text.
- To enter a punctuation mark, symbol, or emoji, just speak its name, such as ”question mark” or ”percent sign” or ”happy emoji.” These may vary by language or dialect.
- To move around and select text, you can use commands like ”Move up two sentences” or ”Move forward one paragraph” or ”Select previous word” or ”Select next paragraph.”
- To format text, try ”Bold that” or ”Capitalize that,” for example. Say ”numeral” to format your next phrase as a number.
- To delete text, you can choose from many delete commands. For example, say “delete that” and Voice Control knows to delete what you just typed. Or say ”Delete all” to delete everything and start over.
Voice Control understands contextual cues, so you can seamlessly transition between text dictation and commands. For example, to dictate and then send a birthday greeting in Messages, you could say ”Happy Birthday. Click Send.” Or to replace a phrase, say ”Replace I’m almost there with I just arrived.”
You can also create your own vocabulary for use with dictation.
Create your own voice commands and vocabulary
Create your own voice commands
![Simplevr speaker independent voice recognition module program for mac free Simplevr speaker independent voice recognition module program for mac free](/uploads/1/2/8/1/128147199/452428314.png)
- Open Voice Control preferences, such as by saying ”Open Voice Control preferences.”
- Click Commands or say ”Click Commands.” The complete list of all commands opens.
- To add a new command, click the add button (+) or say ”Click add.” Then configure these options to define the command:
- When I say: Enter the word or phrase that you want to be able to speak to perform the action.
- While using: Choose whether your Mac performs the action only when you're using a particular app.
- Perform: Choose the action to perform. You can open a Finder item, open a URL, paste text, paste data from the clipboard, press a keyboard shortcut, select a menu item, or run an Automator workflow.
- Use the checkboxes to turn commands on or off. You can also select a command to find out whether other phrases work with that command. For example, “Undo that” works with several phrases, including “Undo this” and “Scratch that.”
To quickly add a new command, you can say ”Make this speakable.” Voice Control will help you configure the new command based on the context. For example, if you speak this command while a menu item is selected, Voice Control helps you make a command for choosing that menu item.
Voice Recognition Module -- Arduino Compatible [AU_VOI_REC ..
Create your own dictation vocabulary
- Open Voice Control preferences, such as by saying ”Open Voice Control preferences.”
- Click Vocabulary, or say ”Click Vocabulary.”
- Click the add button (+) or say ”Click add.”
- Type a new word or phrase as you want it to be entered when spoken.
Learn more
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- For the best performance when using Voice Control with a Mac notebook computer and an external display, keep your notebook lid open or use an external microphone.
- All audio processing for Voice Control happens on your device, so your personal data is always kept private.
- Use Voice Control on your iPhone or iPod touch.
- Learn more about accessibility features in Apple products.
1. Voice Control uses the Siri speech-recognition engine for U.S. English only. Other languages and dialects use the speech-recognition engine previously available with Enhanced Dictation.
Voice Recognition Software Mac - Free Downloads And Reviews ..
2. If you're on a business or school network that uses a proxy server, Voice Control might not be able to download. Have your network administrator refer to the network ports used by Apple software products.