Statistical methods for speech recognition
Author(s)
Bibliographic Information
Statistical methods for speech recognition
（Language, speech, and communication）
MIT Press, c1997
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Includes bibliographical references and indexes
Description and Table of Contents
Description
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.
Table of Contents
- The speech recognition problem
- hidden Markov models
- the acoustic model
- basic language modelling
- the Viterbi search
- hypothesis search on a tree and the fast match
- elements of information theory
- the complexity of tasks - the quality of language models
- the expectation - maximization algorithm and its consequences
- decision trees and tree language models
- phonetics from orthography - spelling-to-base from mappings
- triphones and allophones
- maximum entropy probability estimation and language models
- three applications of maximum entropy estimation to language modelling
- estimation of probabilities from counts and the Back-Off method.
by "Nielsen BookData"