Speech Recognition HOWTO

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. This Book Offers A Unified Vision Of Speech And Language Processing, Presenting State-of-the-art Algorithms And Techniques For Both Speech And Text-based Processing Of Natural Language. This Comprehensive Work Covers Both Statistical And Symbolic Approaches To Language Processing; It Shows How They Can Be Applied To Important Tasks Such As Speech Recognition, Spelling And Grammar Correction, Information Extraction, Search Engines, Machine Translation, And The Creation Of Spoken-language Dialog Agents.--jacket. 1. Introduction -- I. Words. 2. Regular Expressions And Automata. 3. Morphology And Finite-state Transducers. 4. Computational Phonology And Text-to-speech. 5. Probabilistic Models Of Pronunciation And Spelling. 6. N-grams. 7. Hmms And Speech Recognition -- Ii. Syntax. 8. Word Classes And Part-of-speech Tagging. 9. Context-free Grammars For English. 10. Parsing With Context-free Grammars. 11. Features And Unification. 12. Lexicalized And Probabilistic Parsing. 13. Language And Complexity -- Iii. Semantics. 14. Representing Meaning. 15. Semantic Analysis. 16. Lexical Semantics. 17. Word Sense Disambiguation And Information Retrieval -- Iv. Pragmatics. 18. Discourse. 19. Dialogue And Conversational Agents. Daniel Jurafsky And James H. Martin. Includes Bibliographical References (p. 851-902) And Index. This book offers a unified vision of speech and language processing, presenting state-of-the-art algorithms and techniques for both speech and text-based processing of natural language. This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide. - UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELD Covers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin. - EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONS Gives readers an understanding of how language-related algorithms can be applied to important real-world problems. - EMPHASIS ON SCIENTIFIC EVALUATION Offers a description of how systems are evaluated with each problem domain. - EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSING Covers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.

This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models,decision trees, the expectation-maximization algorithm, information theoretic goodness criteria,maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.

Incl. basic language modeling; the expectation-maximization algorithm and its consequences; triphones & allophones etc.

Provides a theoretically sound, technically accurate, and complete description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine. Covers production, perception, and acoustic-phonetic characterization of the speech signal; signal processing and analysis methods for speech recognition; pattern comparison techniques; speech recognition system design and implementation; theory and implementation of hidden Markov models; speech recognition based on connected word models; large vocabulary continuous speech recognition; and task- oriented application of automatic speech recognition. For practicing engineers, scientists, linguists, and programmers interested in speech recognition. This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora."Methodology" boxes are included in each chapter. Each chapter is built around one or more worked examples" to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing. This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques. The material in this book is intended as a one-semester course in speech processing. The purpose of this text is to show how digital signal processing techniques can be applied to problems related to speech communication. The book gives an extensive description of the physical basis for speech coding including fourier analysis, digital representation and digital and time domain models of the wave form. It goes on to discuss homomorphic speech processing, linear predictive coding and digital processing for machine communication by voice. Table of Contents......Page 2 1.3. Trademarks......Page 4 2.4. ToDo......Page 5 2.5. Revision History......Page 6 3.1. Speech Recognition Basics......Page 7 3.3. Uses and Applications......Page 8 4.3. Computers/Processors......Page 10 5.1.2. CVoiceControl/kVoiceControl......Page 12 5.1.6. CMU Sphinx......Page 13 5.1.11. More Free Software?......Page 14 5.2.4. SpeechWorks......Page 15 5.2.8. More Commercial Products......Page 16 6.1. How Recognizers Work......Page 17 6.2. Digital Audio Basics......Page 18 7.2. Internet......Page 19 "This book offers a unified vision of speech and language processing, presenting state-of-the-art algorithms and techniques for both speech and text-based processing of natural language. This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents."--BOOK JACKET.

The purpose of this book is to show how digital signal processing techniques can be applied to problems related to speech communication. The book gives an extensive description of the physical basis for speech coding including fourier analysis, digital representation and digital and time domain models of the waveform. It goes on to discuss homomorphic speech processing, linear predictive coding and digital processing for machine communication by voice.

A theoretical, technical description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine. The book covers areas including production, perception and acoustic-phonetic characterization of the speech signal and signal processing recognition Lawrence Rabiner, Biing-hwang Juang. Includes Bibliographical References And Index.