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Overview
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.
Synopsis
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.
- Task-oriented application of automatic speech recognition.
Discusses the breadth and depth of the various disciplines that are required for a deep understanding of all aspects of speech recognition.
Explores the relative advantages and disadvantages of the various approach to speech recognition, and shows why, on balance, the pattern recognition approach has become the method of choice for most modern systems.
Outlines the fundamental techniques used to provide the speech features used in all recognition systems.
Deals with the fundamental problems of defining speech feature vector patterns, and comparing pairs of feature vector patterns both locally and globally.
Discusses the key issues of training a speech recognizer and adapting the recognizer parameters to different speakers, transmission conditions, and speaking environments.
Describes a basic set of statistical modeling techniques for characterizing speech.
Extends the speech recognition problem from single word sequences to fluent speech.
Considers the basic principles that make some tasks successful while other fail. Provides examples of several task-oriented recognizers and how they perform in practice.
Booknews
The authors discuss fundamentals of speech recognition, the speech signal, signal processing and analysis methods for speech recognition, pattern-comparison techniques, speech recognition system design and implementation issues, theory and implementation of hidden Markov models, speech recognition based on connected work models, large vocabulary continuous speech recognition, and task oriented applications of automatic speech recognition. Chapters contain review exercises with solutions. Annotation c. Book News, Inc., Portland, OR (booknews.com)