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Hardcover | $62.00 X | £45.95 | ISBN: 9780262100663 | 305 pp. | 6.2 x 9 in | January 1998

Statistical Methods for Speech Recognition

Overview

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.

Endorsements

“This book fills a long-existing gap in the scientific literature on automatic speech recognition. During the past three decades, statistical methods have had the strongest impact on the whole area of automatic speech recognition, in particular for large-vocabulary systems. This is without doubt the first book giving both a comprehensive overview and an in-depth description of these methods. The authot is one the pioneers who has been active in this field for more than 25 years.”
Professor Hermann Ney, Computer Science Department, RWTH Aachen, University of Technology
“Frederick Jelinek is one of the few true pioneers of modern speech recognition technology. This book will be an essential reference book for all students and engineers working in the speech recognition area. More than that, it will also serve as a testament to Frederick Jelinek's own achievements in the field which span more than 25 years and which include so much that is core to modern-day speech recognition technology.”
Steve Young, Professor of Information Engineering, Engineering Department, Cambridge University, England
“For the first time, researchers in this field will have a book that will serve as the bible for many aspects of language and speech processing. Frankly, I can't imagine a person working in this field not wanting to have a personal copy.”
Victor Zue, MIT Laboratory for Computer Science