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 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.

Frederick Jelinek is Professor of Electrical and Computer Engineering and Director of the Center for Language and Speech Processing at Johns Hopkins University. For over twenty years he led the IBM Continuous Speech Recognition Group.

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