Differences between version 3 and revision by previous author of HowToSpeechRecognitionHOWTO.
Other diffs: Previous Major Revision, Previous Revision, or view the Annotated Edit History
| Newer page: | version 3 | Last edited on Monday, October 25, 2004 5:08:08 am | by AristotlePagaltzis | Revert |
| Older page: | version 2 | Last edited on Saturday, July 20, 2002 12:04:56 am | by !PerryLorier | Revert |
@@ -1,1260 +1 @@
-Speech Recognition HOWTO
-!!!Speech Recognition HOWTO
-!Stephen Cook
-
- scook@gear21.com
-
-
-
-__Revision History__Revision v2.0April 19, 2002Revised by: sccChanged license information (now GFDL) and added a new publication.Revision v1.2February 5, 2002Revised by: sccAdded more commercial software listings (sent by Mayur Patel).Revision v1.1October 5, 2001Revised by: sccAdded info for Vocalis Speechware. Fixed/Updated various other items.Revision v1.0November 20, 2000Revised by: sccAdded info on L and H and HTKRevision v0.5September 13, 2000Revised by: sccInitial HOWTO Submission
-
-
-
-
-
-Automatic Speech Recognition (ASR) on Linux is becoming easier.
-Several packages are available for users as well as developers.
-This document describes the basics of speech recognition and
-describes some of the available software.
-
-
-
-
-
-
-----; __Table of Contents__; 1. Legal Notices: ; 1.1. Copyright/License; 1.2. Disclaimer; 1.3. Trademarks; 2. Forward: ; 2.1. About This Document; 2.2. Acknowledgements; 2.3. Comments/Updates/Feedback; 2.4. !ToDo; 2.5. Revision History; 3. Introduction: ; 3.1. Speech Recognition Basics; 3.2. Types of Speech Recognition; 3.3. Uses and Applications; 4. Hardware: ; 4.1. Sound Cards; 4.2. Microphones; 4.3. Computers/Processors; 5. Speech Recognition Software: ; 5.1. Free Software; 5.2. Commercial Software; 6. Inside Speech Recognition: ; 6.1. How Recognizers Work; 6.2. Digital Audio Basics; 7. Publications: ; 7.1. Books; 7.2. Internet
-!!!1. Legal Notices
-!!1.1. Copyright/License
-
-Copyright (c) 2000-2002 Stephen C. Cook.
-Permission is granted to copy, distribute, and/or modify this document under the
-terms of the GNU Free Documentation License, Version 1.1 or any later version published
-by the Free Software Foundation.
-
-
-
-This document is made available under the terms of the GNU Free Documentation License (GFDL), which is hereby
-incorporated by reference.
-
-----
-!!1.2. Disclaimer
-
-The author disclaims all warranties with regard to this document,
-including all implied warranties of merchantability and fitness for a
-certain purpose; in no event shall the author be liable for any
-special, indirect or consequential damages or any damages whatsoever
-resulting from loss of use, data or profits, whether in an action of
-contract, negligence or other tortious action, arising out of or in
-connection with the use of this document.
-
-----
-!!1.3. Trademarks
-
-All trademarks contained in this document are copyright/trademark
-of their respective owners.
-
-----
-!!!2. Forward
-!!2.1. About This Document
-
-This document is targeted at the beginner to intermediate level Linux
-user interested in learning about Speech Recognition and trying it out.
-It may also help the interested developer in explaining the basics of
-speech recognition programming.
-
-
-
-I started this document when I began researching what speech
-recognition software and development libraries were available for Linux.
-Automated Speech Recognition (ASR or just SR) on Linux is just starting
-to come into its own, and I hope this document gives it a push in the
-right direction - by supporting both users and developers of ASR
-technology.
-
-
-
-I have left a variety of SR techniques out of this document, and
-instead I have focused on the "HOWTO" aspect (since this is a howto...).
-I have included a Publications section so the interested reader can
-find books and articles on anything not covered
here. This is not
-meant to be a definitive statement of ASR on Linux.
-
-
-
-For the most recent version of this document, check the LDP archive,
-or go to:
-http://www.gear21.com/speech/index.html.
-
-----
-!!2.2. Acknowledgements
-
-I would like to thank the following people for the help, reviewing,
-and support of this document:
-
-
-
-
-
-
-
-
-****
-
- Jessica Perry Hekman
-
-
-
-****
-****
-
- Geoff Wexler
-
-
-
-****
-
-----
-!!2.3. Comments/Updates/Feedback
-
-If you have any comments, suggestions, revisions, updates, or just
-want to chat about ASR, please send an email to me at
-scook@gear21.com.
-
-----
-!!2.4. !ToDo
-
-The following things are left "to do":
-
-
-
-
-
-
-
-
-****
-
- Add descriptions in the Publications section.
-
-
-
-****
-****
-
- Add more books to the Publications section.
-
-
-
-****
-****
-
- Add more links with descriptions.
-
-
-
-****
-****
-
- Enhance the description of the ASR system steps
-
-
-
-****
-****
-
- Include descriptions of FFTs and Filters.
-
-
-
-****
-****
-
- Include descriptions of DSP principles.
-
-
-
-****
-
-----
-!!2.5. Revision History
-
-v0.1 first rough draft - August 2000
-
-
-
-v0.5 final draft - September 2000
-
-----
-!!!3. Introduction
-!!3.1. Speech Recognition Basics
-
-
-Speech recognition is the process by which a computer (or
-other type of machine) identifies spoken words. Basically, it means
-talking to your computer, AND having it correctly recognize what you
-are saying.
-
-
-
-The following definitions are the basics needed for understanding
-speech recognition technology.
-
-
-
-
-
-
-
-; Utterance:
-
- An utterance is the vocalization (speaking) of a word or words that
-represent a single meaning to the computer. Utterances can be a
-single word, a few words, a sentence, or even multiple sentences.
-
-
-; Speaker Dependance:
-
- Speaker dependent systems are designed around a specific speaker.
-They generally are more accurate for the correct speaker, but much
-less accurate for other speakers. They assume the speaker will
-speak in a consistent voice and tempo. Speaker independent systems
-are designed for a variety of speakers. Adaptive systems usually start
-as speaker independent systems and utilize training techniques to
-adapt to the speaker to increase their recognition accuracy.
-
-
-; Vocabularies:
-
- Vocabularies (or dictionaries) are lists of words or utterances that
-can be recognized by the SR system. Generally, smaller vocabularies
-are easier for a computer to recognize, while larger vocabularies
-are more difficult. Unlike normal dictionaries, each entry doesn't
-have to be a single word. They can be as long as a sentence or two.
-Smaller vocabularies can have as few as 1 or 2 recognized utterances
-(e.g."Wake Up"), while very large vocabularies can have a hundred
-thousand or more!
-
-
-; Accuract:
-
- The ability of a recognizer can be examined by measuring its
-accuracy - or how well it recognizes utterances. This includes not
-only correctly identifying an utterance but also identifying if the
-spoken utterance is not in its vocabulary. Good ASR systems have an
-accuracy of 98% or more! The acceptable accuracy of a system
-really depends on the application.
-
-
-; Training:
-
- Some speech recognizers have the ability to adapt to a speaker.
-When the system has this ability, it may allow training to take
-place. An ASR system is trained by having the speaker repeat
-standard or common phrases and adjusting its comparison algorithms
-to match that particular speaker. Training a recognizer usually
-improves its accuracy.
-
-
-
-
- Training can also be used by speakers that have difficulty
-speaking, or pronouncing certain words. As long as the speaker
-can consistently repeat an utterance, ASR systems with training
-should be able to adapt.
-
-
-
-
-----
-!!3.2. Types of Speech Recognition
-
-
-Speech recognition systems can be separated in several different
-classes by describing what types of utterances they have the ability
-to recognize. These classes are based on the fact that one of the
-difficulties of ASR is the ability to determine when a speaker starts
-and finishes an utterance. Most packages can fit into more than one
-class, depending on which mode they're using.
-
-
-
-
-
-
-
-; Isolated Words:
-
- Isolated word recognizers usually require each utterance to have
-quiet (lack of an audio signal) on BOTH sides of the sample window.
-It doesn't mean that it accepts single words, but does require
-a single utterance at a time. Often, these systems have
-"Listen/Not-Listen" states, where they require the speaker to wait
-between utterances (usually doing processing during the pauses).
-Isolated Utterance might be a better name for this class.
-
-
-; Connected Words:
-
- Connect word systems (or more correctly 'connected utterances')
-are similar to Isolated words, but allow separate utterances to be
-'run-together' with a minimal pause between them.
-
-
-; Continuous Speech:
-
- Continuous recognition is the next step. Recognizers with continuous
-speech capabilities are some of the most difficult to create because
-they must utilize special methods to determine utterance boundaries.
-Continuous speech recognizers allow users to speak almost naturally,
-while the computer determines the content. Basically, it's computer
-dictation.
-
-
-; Spontaneous Speech:
-
- There appears to be a variety of definitions for what spontaneous
-speech actually is. At a basic level, it can be thought of as
-speech that is natural sounding and not rehearsed. An ASR system
-with spontaneous speech ability should be able to handle a variety
-of natural speech features such as words being run together, "ums"
-and "ahs", and even slight stutters.
-
-
-; Voice Verification/Identification:
-
- Some ASR systems have the ability to identify specific users. This
-document doesn't cover verification or security systems.
-
-
-
-
-----
-!!3.3. Uses and Applications
-
-
-Although any task that involves interfacing with a computer can
-potentially use ASR, the following applications are the most
-common right now.
-
-
-
-
-
-
-
-; Dictation:
-
- Dictation is the most common use for ASR systems today. This
-includes medical transcriptions, legal and business dictation, as
-well as general word processing. In some cases special vocabularies
-are used to increase the accuracy of the system.
-
-
-; Command and Control:
-
- ASR systems that are designed to perform functions and actions on the
-system are defined as Command and Control systems. Utterances like
-"Open Netscape" and "Start a new xterm" will do just that.
-
-
-; Telephony:
-
- Some PBX/Voice Mail systems allow callers to speak commands instead of
-pressing buttons to send specific tones.
-
-
-; Wearables:
-
- Because inputs are limited for wearable devices, speaking is a
-natural possibility.
-
-
-; Medical/Disabilities:
-
- Many people have difficulty typing due to physical limitations such
-as repetitive strain injuries (RSI), muscular dystrophy, and
-many others. For example, people with difficulty hearing could use
-a system connected to their telephone to convert the caller's speech
-to text.
-
-
-; Embedded Applications:
-
- Some newer cellular phones include C8C speech recognition that allow
-utterances such as "Call Home". This could be a major factor in the
-future of ASR and Linux. Why can't I talk to my television yet?
-
-
-
-
-----
-!!!4. Hardware
-!!4.1. Sound Cards
-
-
-Because speech requires a relatively low bandwidth, just about any
-medium-high quality 16 bit sound card will get the job done. You must
-have sound enabled in your kernel, and you must have correct drivers
-installed. For more information on sound cards, please see "The Linux
-Sound HOWTO" available at: http://www.!LinuxDoc.org/. Sound card
-quality often starts a heated discussion about their impact on accuracy
-and noise.
-
-
-
-Sound cards with the 'cleanest' A/D (analog to digital) conversions
-are recommended, but most often the clarity of the digital sample is
-more dependent on the microphone quality and even more dependent on the
-environmental noise. Electrical "noise" from monitors, pci slots,
-hard-drives, etc. are usually nothing compared to audible noise
-from the computer fans, squeaking chairs, or heavy breathing.
-
-
-
-Some ASR software packages may require a specific sound card. It's
-usually a good idea to stay away from specific hardware requirements,
-because it limits many of your possible future options and decisions.
-You'll have to weigh the benefits and costs if you are considering
-packages that require specific hardware to function properly.
-
-----
-!!4.2. Microphones
-
-A quality microphone is key when utilizing ASR. In most cases, a
-desktop microphone just won't do the job. They tend to pick up more
-ambient noise that gives ASR programs a hard time.
-
-
-
-Hand held microphones are also not the best choice as they can be
-cumbersome to pick up all the time. While they do limit the amount
-of ambient noise, they are most useful in applications that require
-changing speakers often, or when speaking to the recognizer isn't
-done frequently (when wearing a headset isn't an option).
-
-
-
-
-The best choice, and by far the most common is the headset style.
-It allows the ambient noise to be minimized, while allowing you to
-have the microphone at the tip of your tongue all the time. Headsets
-are available without earphones and with earphones (mono or stereo).
-I recommend the stereo headphones, but it's just a matter of personal
-taste.
-
-
-
-You can get excellent quality microphone headsets for between $25
-$100. A good place to start looking is http://www.headphones.com or
-http://www.speechcontrol.com.
-
-
-
-
-A quick note about levels: Don't forget to turn up your microphone
-volume. This can be done with a program such as XMixer or OSS Mixer
-and care should be used to avoid feedback noise. If the ASR software
-includes auto-adjustment programs, use them instead, as they are
-optimized for their particular recognition system.
-
-----
-!!4.3. Computers/Processors
-
-ASR applications can be heavily dependent on processing speed. This
-is because a large amount of digital filtering and signal processing
-can take place in ASR.
-
-
-
-As with just about any cpu intensive software, the faster the better.
-Also, the more memory the better. It's possible to do some SR with 100MHz
-and 16M RAM, but for fast processing (large dictionaries, complex
-recognition schemes, or high sample rates), you should shoot for a
-minimum of a 400MHz and 128M RAM. Because of the processing required,
-most software packages list their minimum requirements.
-
-
-
-Using a cluster (Beowulf or otherwise) to perform massive recognition
-efforts hasn't yet been undertaken. If you know of any project underway,
-or in development please send me a note! scook@gear21.com
-
-----
-!!!5. Speech Recognition Software
-!!5.1. Free Software
-
-Much of the free software listed here is available for download at:
-http://sunsite.uio.no/pub/Linux/sound/apps/speech/
-
-
-----
-!5.1.1. XVoice
-
-XVoice is a dictation/continuous speech recognizer that can be used
-with a variety of XWindow applications. It allows user-defined macros.
-This is a fine program with a definite future. Once setup, it
-performs with adequate accuracy.
-
-
-
-XVoice requires that you download and install IBM's (free) !ViaVoice
-for Linux (See Commercial Section). It also requires the configuration
-of !ViaVoice to work correctly. Additionally, Lesstif/Motif (libXm) is
-required. It is also important to note that because this program
-interacts with X windows, you must leave X resources open on your
-machine, so caution should be used if you use this on a networked or
-multi-user machine.
-
-
-
-This software is primarily for users. An RPM is available.
-
-
-
-!!HomePage: http://www.compapp.dcu.ie/~tdoris/Xvoice/
-http://www.zachary.com/creemer/xvoice.html
-
-
-
-Project: http://xvoice.sourceforge.net
-
-
-
-Community: http://www.onelist.com/community/xvoice
-
-----
-!5.1.2. CVoiceControl/kVoiceControl
-
-CVoiceControl (which stands for Console Voice Control) started its
-life as KVoiceControl (KDE Voice Control). It is a basic speech
-recognition system that allows a user to execute Linux commands by
-using spoken commands. CVoiceControl replaces KVoiceControl.
-
-
-
-The software includes a microphone level configuration utility,
-a vocabulary "model editor" for adding new commands and utterances,
-and the speech recognition system.
-
-
-
-CVoiceControl is an excellent starting point for experienced users
-looking to get started in ASR. It is not the most user friendly,
-but once it has been trained correctly, it can be very helpful. Be
-sure to read the documentation while setting up.
-
-
-
-This software is primarily for users.
-
-
-
-Homepage: http://www.kiecza.de/daniel/linux/index.html
-
-
-
-Documents: http://www.kiecza.de/daniel/linux/cvoicecontrol/index.html
-
-----
-!5.1.3. Open Mind Speech
-
-Started in late 1999, Open Mind Speech has changed names several times
-(was !VoiceControl, then !SpeechInput, and then !FreeSpeech), and is now
-part of the "Open Mind Initiative". This is an open source project.
-Currently it isn't completely operational and is primarily for developers.
-
-
-
-This software is primarily for developers.
-
-
-
-Homepage: http://freespeech.sourceforge.net
-
-----
-!5.1.4. GVoice
-
-GVoice is a speech ASR library that uses IBM's !ViaVoice (free) SDK
-to control Gtk/GNOME applications. It includes libraries for
-initialization, recognition engine, vocabulary manipulation, and panel
-control. Development on this has been idle for over a year.
-
-
-
-This software is primarily for developers.
-
-
-
-Homepage: http://www.cse.ogi.edu/~omega/gnome/gvoice/
-
-----
-!5.1.5. ISIP
-
-The Institute for Signal and Information Processing at Mississippi
-State University has made its speech recognition engine available. The
-toolkit includes a front-end, a decoder, and a training module. It's a
-functional toolkit.
-
-
-
-This software is primarily for developers.
-
-
-
-The toolkit (and more information about ISIP) is available at:
-http://www.isip.msstate.edu/project/speech/
-
-----
-!5.1.6. CMU Sphinx
-
-Sphinx originally started at CMU and has recently been released as
-open source. This is a fairly large program that includes a lot of
-tools and information. It is still "in development", but includes
-trainers, recognizers, acoustic models, language models, and some
-limited documentation.
-
-
-
-This software is primarily for developers.
-
-
-
-Homepage: http://www.speech.cs.cmu.edu/sphinx/Sphinx.html
-
-
-
-Source: http://download.sourceforge.net/cmusphinx/sphinx2-.1a.tar.gz
-
-----
-!5.1.7. Ears
-
-Although Ears isn't fully developed, it is a good starting
-point for programmers wishing to start in ASR.
-
-
-
-This software is primarily for developers.
-
-
-
-FTP site: ftp://svr-ftp.eng.cam.ac.uk/comp.speech/recognition/
-
-----
-!5.1.8. NICO ANN Toolkit
-
-The NICO Artificial Neural Network toolkit is a flexible back
-propagation neural network toolkit optimized for speech recognition
-applications.
-
-
-
-This software is primarily for developers.
-
-
-
-Its homepage: http://www.speech.kth.se/NICO/index.html
-
-----
-!5.1.9. Myers' Hidden Markov Model Software
-
-This software by Richard Myers is HMM algorithms written in C++ code.
-It provides an example and learning tool for HMM models described in
-the L. Rabiner book "Fundamentals of Speech Recognition".
-
-
-
-This software is primarily for developers.
-
-
-
-Information is available at:
-http://www.itl.atr.co.jp/comp.speech/Section6/Recognition/myers.hmm.html
-
-----
-!5.1.10. Jialong He's Speech Recognition Research Tool
-
-Although not originally written for Linux, this research tool can be
-compiled on Linux. It contains three different types of recognizers:
-DTW, Dynamic Hidden Markov Model, and a Continuous Density Hidden
-Markov Model. This is for research and development uses, as it is
-not a fully functional ASR system. The toolkit contains some very
-useful tools.
-
-
-
-This software is primarily for developers.
-
-
-
-More information is available at:
-http://www.itl.atr.co.jp/comp.speech/Section6/Recognition/jialong.html
-
-----
-!5.1.11. More Free Software?
-
-If you know of free software that isn't included in the above list,
-please send me a note at: scook@gear21.com. If you're in the mood,
-you can also send me where to get a copy of the software, and any
-impressions you may have about it. Thanks!
-
-----
-!!5.2. Commercial Software
-!5.2.1. IBM !ViaVoice
-
-IBM has made true on their promise to support Linux with their series
-of !ViaVoice products for Linux, though the future of their SDKs aren't
-set in stone (their licensing agreement for developers isn't officially
-released as of this date - more to come).
-
-
-
-Their commercial (not-free) product, IBM !ViaVoice Dictation for Linux
-(available at http://www-4.ibm.com/software/speech/linux/dictation.html)
-performs very well, but has some sizeable system requirements compared
-to the more basic ASR systems (64M RAM and 233MHz Pentium). For the
-$59.95US price tag you also get an Andrea NC-8 microphone. It also
-allows multiple users (but I haven't tried it with multiple users, so
-if anyone has any experience please give me a shout). The package
-includes: documentation (PDF), Trainer, dictation system, and
-installation scripts. Support for additional Linux Distributions based
-on 2.2 kernels is also available in the latest release.
-
-
-
- The ASR SDK is available for free, and includes IBM's SMAPI, grammar
-API, documentation, and a variety of sample programs. The !ViaVoice
-Run Time Kit provides an ASR engine and data files for dictation
-functions, and user utilities. The !ViaVoice Command 8 Control Run Time
-Kit includes the ASR engine and data files for command and control
-functions, and user utilities. The SDK and Kits require 128M RAM and
-a Linux 2.2 or better kernel)
-
-
-
-The SDKs and Kits are available for free at:
-http://www-4.ibm.com/software/speech/dev/sdk_linux.html
-
-----
-!5.2.2. Vocalis Speechware
-
-More information on Vocalis and Vocalis Speechware is available at:
-http://www.vocalisspeechware.com and
-http://www.vocalis.com.
-
-----
-!5.2.3. Babel Technologies
-
-Babel Technologies has a Linux SDK available called Babear. It is a speaker-independent
-system based on Hybrid Markov Models and Artificial Neural Networks technology. They also
-have a variety of products for Text-to-speech, speaker verification, and phoneme analysis.
-More information is available at: http://www.babeltech.com.
-
-----
-!5.2.4. !SpeechWorks
-
-I didn't see anything on their website that specifically mentioned Linux, but their
-"!OpenSpeech Recognizer" uses VoiceXML, which is an open standard.
-More information is available at: http://www.speechworks.com.
-
-----
-!5.2.5. Nuance
-
-Nuance offers a speech recognition/natural language product (currently Nuance 8.) for
-a variety of *nix platforms. It can handle very large vocabularies and uses a unqiue
-distributed architecture for scalability and fault tolerance.
-More information is available at: http://www.nuance.com.
-
-----
-!5.2.6. Abbot/!AbbotDemo
-
-Abbot is a very large vocabulary, speaker independent ASR system.
-It was originally developed by the Connectionist Speech Group at
-Cambridge University. It was transferred (commercialized) to
-!SoftSound. More information is available at:
-http://www.softsound.com.
-
-
-
-!AbbotDemo is a demonstration package of Abbot. This demo system
-has a vocabulary of about 5000 words and uses the connectionist/HMM
-continuous speech algorithm. This is a demonstration program with no
-source code.
-
-----
-!5.2.7. Entropic
-
-The fine people over at Entropic have been bought out by Micro$oft...
-Their products and support services have all but disappeared. Their
-support for HTK and ESPS/waves+ is gone, and their future is in the
-hands of M$. Their old website as http://www.entropic.com has more
-information.
-
-
-
-K.K. Chin advised me that the original developers of the HTK (the
-Speech Vision and Robotic Group at Cambridge) are still
-providing support for it. There is also a "free" version
-available at: http://htk.eng.cam.ac.uk.
-Also note that Microsoft still owns the copyright to the current
-HTK code...
-
-
-----
-!5.2.8. More Commercial Products
-
-There are rumors of more commercial ASR products becoming available
-in the near future (including L8H). I talked with a couple of
-L8H representatives at Comdex 2000 (Vegas) and none of them could give
-me any information on a Linux release, or even if they planned on releasing
-any products for Linux. If you have any further information, please send
-any details to me at scook@gear21.com.
-
-----
-!!!6. Inside Speech Recognition
-!!6.1. How Recognizers Work
-
-
-Recognition systems can be broken down into two main types. Pattern
-Recognition systems compare patterns to known/trained patterns to
-determine a match. Acoustic Phonetic systems use knowledge of the
-human body (speech production, and hearing) to compare speech features
-(phonetics such as vowel sounds). Most modern systems focus on the
-pattern recognition approach because it combines nicely with current
-computing techniques and tends to have higher accuracy.
-
-
-
-Most recognizers can be broken down into the following steps:
-
-
-
-
-
-
-
-
-***#
-
- Audio recording and Utterance detection
-
-
-
-***#
-***#
-
- Pre-Filtering (pre-emphasis, normalization, banding, etc.)
-
-
-
-***#
-***#
-
- Framing and Windowing (chopping the data into a usable format)
-
-
-
-***#
-***#
-
- Filtering (further filtering of each window/frame/freq. band)
-
-
-
-***#
-***#
-
- Comparison and Matching (recognizing the utterance)
-
-
-
-***#
-***#
-
- Action (Perform function associated with the recognized pattern)
-
-
-
-***#
-
-
-
-
-Although each step seems simple, each one can involve a multitude of
-different (and sometimes completely opposite) techniques.
-
-
-
-(1) Audio/Utterance Recording: can be accomplished in a number of ways.
-Starting points can be found by comparing ambient audio levels (acoustic
-energy in some cases) with the sample just recorded. Endpoint detection
-is harder because speakers tend to leave "artifacts" including
-breathing/sighing,teeth chatters, and echoes.
-
-
-
-(2) Pre-Filtering: is accomplished in a variety of ways, depending on
-other features of the recognition system. The most common methods are
-the "Bank-of-Filters" method which utilizes a series of audio filters to
-prepare the sample, and the Linear Predictive Coding method which uses
-a prediction function to calculate differences (errors). Different
-forms of spectral analysis are also used.
-
-
-
-(3) Framing/Windowing involves separating the sample data into
-specific sizes. This is often rolled into step 2 or step 4. This step
-also involves preparing the sample boundaries for analysis (removing
-edge clicks, etc.)
-
-
-
-(4) Additional Filtering is not always present. It is the final
-preparation for each window before comparison and matching. Often this
-consists of time alignment and normalization.
-
-
-
-There are a huge number of techniques available for (5), Comparison
-and Matching. Most involve comparing the current window with known
-samples. There are methods that use Hidden Markov Models (HMM),
-frequency analysis, differential analysis, linear algebra
-techniques/shortcuts, spectral distortion, and time distortion methods.
-All these methods are used to generate a probability and accuracy match.
-
-
-
-(6) Actions can be just about anything the developer wants. *GRIN*
-
-----
-!!6.2. Digital Audio Basics
-
-Audio is inherently an analog phenomenon. Recording a digital sample
-is done by converting the analog signal from the microphone to an
-digital signal through the A/D converter in the sound card. When a
-microphone is operating, sound waves vibrate the magnetic element in
-the microphone, causing an electrical current to the sound card (think
-of a speaker working in reverse). Basically, the A/D converter records
-the value of the electrical voltage at specific intervals.
-
-
-
-There are two important factors during this process. First is the
-"sample rate", or how often to record the voltage values. Second, is
-the "bits per sample", or how accurate the value is recorded. A third
-item is the number of channels (mono or stereo), but for most ASR
-applications mono is sufficient. Most applications use pre-set values
-for these parameters and user's shouldn't change them unless the
-documentation suggests it. Developers should experiment with different
-values to determine what works best with their algorithms.
-
-
-
-So what is a good sample rate for ASR? Because speech is relatively
-low bandwidth (mostly between 100Hz-8kHz), 8000 samples/sec (8kHz) is
-sufficient for most basic ASR. But, some people prefer 16000
-samples/sec (16kHz) because it provides more accurate high frequency
-information. If you have the processing power, use 16kHz. For most
-ASR applications, sampling rates higher than about 22kHz is a waste.
-
-
-
-And what is a good value for "bits per sample"? 8 bits per sample
-will record values between 0 and 255, which means that the position
-of the microphone element is in one of 256 positions. 16 bits per
-sample divides the element position into 65536 possible values.
-Similar to sample rate, if you have enough processing power and
-memory, go with 16 bits per sample. For comparison, an audio
-Compact Disc is encoded with 16 bits per sample at about 44kHz.
-
-
-
-The encoding format used should be simple - linear signed or
-unsigned. Using a U-Law/A-Law algorithm or some other compression
-scheme is usually not worth it, as it will cost you in computing power,
-and not gain you much.
-
-----
-!!!7. Publications
-
-If there is a publication that is not on this list, that you think
-should be, please send the information to me at: scook@gear21.com.
-
-----
-!!7.1. Books
-
-
-
-
-
-
-
-****
-
- "Fundamentals of Speech Recognition". L. Rabiner 8 B. Juang. 1993.
-ISBN: 0130151572.
-
-
-
-****
-****
-
- "How to Build a Speech Recognition Application". B. Balentine,
-D. Morgan, and W. Meisel. 1999. ISBN: 0967127815.
-
-
-
-****
-****
-
-
-"Speech Recognition : Theory and C++ Implementation". C. Becchetti
-and L.P. Ricotti. 1999. ISBN: 0471977306.
-
-
-
-****
-****
-
- "Applied Speech Technology". A. Syrdal, R. Bennett, S. Greenspan.
-1994. ISBN: 0849394562.
-
-
-
-****
-****
-
- "Speech Recognition : The Complete Practical Reference Guide".
-P. Foster, T. Schalk. 1993. ISBN: 0936648392.
-
-
-
-****
-****
-
- "Speech and Language Processing: An Introduction to Natural Language
-Processing, Computational Linguistics and Speech Recognition".
-D. Jurafsky, J. Martin. 2000. ISBN: 0130950696.
-
-
-
-****
-****
-
- "Discrete-Time Processing of Speech Signals (IEEE Press Classic
-Reissue)". J. Deller, J. Hansen, J. Proakis. 1999.
-ISBN: 0780353862.
-
-
-
-****
-****
-
- "Statistical Methods for Speech Recognition (Language, Speech, and
-Communication)". F. Jelinek. 1999. ISBN: 0262100665.
-
-
-
-****
-****
-
- "Digital Processing of Speech Signals" L. Rabiner, R. Schafer. 1978.
-ISBN: 0132136031
-
-
-
-****
-****
-
- "Foundations of Statistical Natural Language Processing".
-C. Manning, H. Schutze. 1999. ISBN: 0262133601.
-
-
-
-****
-****
-
- "Designing Effective Speech Interfaces".
-S. Weinschenk, D. T. Barker. 2000. ISBN: 0471375454.
-
-
-
-****
-
-
-
- For a very LARGE online biography, check the Institut Fur Phonetik:
-http://www.informatik.uni-frankfurt.de/~ifb/bib_engl.html
-
-----
-!!7.2. Internet
-
-
-
-
-
-; news:comp.speech:
-
- Newsgroup dedicated to computer and speech.
-
-
-
-
-
-****
-
- US: http://www.speech.cs.cmu.edu/comp.speech/
-
-
-
-****
-****
-
- UK: http://svr-www.eng.cam.ac.uk/comp.speech/
-
-
-
-****
-****
-
- Aus: http://www.speech.su.oz.au/comp.speech/
-
-
-
-****
-
-
-; news:comp.speech.users:
-
- Newsgroup dedicated to users of speech software.
-
-
-
-
-
-
-
-
-
-****
-
- http://www.speechtechnology.com/users/comp.speech.users.html
-
-
-
-****
-
-
-; news:comp.speech.research:
-
- Newsgroup dedicated to speech software and hardware research.
-
-
-; news:comp.dsp:
-
- Newsgroup dedicated to digital signal processing.
-
-
-; news:alt.sci.physics.acoustics:
-
- Newsgroup dedicated to the physics of sound.
-
-
-; DDLinux Email List:
-
- Speech Recognition on Linux Mailing List.
-
-
-
-
-
-****
-
- Homepage: http://leb.net/ddlinux/
-
-
-
-****
-****
-
- Archives: http://leb.net/pipermail/ddlinux/
-
-
-
-****
-
-
-; Linux Software Repository for speech applications:
-
- http://sunsite.uio.no/pub/linux/sound/apps/speech/
-
-
-; Russ Wilcox's List of Speech Recognition Links:
-
- (excellent) http://www.tiac.net/users/rwilcox/speech.html
-
-
-; Online Bibliography:
-
- Online Bibliography of Phonetics and Speech Technology Publications.
-http://www.informatik.uni-frankfurt.de/~ifb/bib_engl.html
-
-
-; MIT's Spoken Language Systems Homepage:
-
- http://www.sls.lcs.mit.edu/sls/
-
-
-; Oregon Graduate Institute:
-
- Center for Spoken Language Understanding at Oregon Graduate
-Institute. An excellent location for developers and researchers.
-http://cslu.cse.ogi.edu/
-
-
-; IBM's !ViaVoice Linux SDK:
-
- http://www-4.ibm.com/software/speech/dev/sdk_linux.html
-
-
-; Mississippi State:
-
- Mississippi State Institute for Signal and Information Processing
-homepage with a large amount of useful information for developers.
-http://www.isip.msstate.edu/projects/speech/
-
-
-; Speech Technology:
-
- ASR software and accessories.
-http://www.speechtechnology.com
-
-
-; Speech Control:
-
- Speech Controlled Computer Systems. Microphones, headsets, and
-wireless products for ASR.
-http://www.speechcontrol.com
-
-
-; Microphones.com:
-
- Microphones and accessories for ASR.
-http://www.microphones.com
-
-
-; 21st Century Eloquence:
-
- "Speech Recognition Specialists."
-http://voicerecognition.com
-
-
-; Computing Out Loud:
-
- Primarily for Windows users, but good info.
-http://www.out-loud.com
-
-
-; Say I Can.com:
-
- "The Speech Recognition Information Source."
-http://www.sayican.com
+Describe [HowToSpeechRecognitionHOWTO]
here.
