Statistical Methods for Speech Recognition (Language, Speech, and Communication)
by Frederick Jelinek
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Book Summary Information
Author: Frederick JelinekEdition: Hardcover
Audio: English (Original Language); English (Unknown); English (Published)
Published: 1998-01-16
ISBN: 0262100665
Number of pages: 305
Publisher: The MIT Press
Book Reviews of Statistical Methods for Speech Recognition (Language, Speech, and Communication)
Book Review: Excellent for experts
Summary: 5 Stars
I bought this book because I wanted a comprehensive introduction on the statistical approach to speech recognition. There is no doubt that this is an excellent book, that achieves this. If you are new to the field of speech recognition, be warned that this book isn't exactly the easiest to read, though.
For example, chapter 2 which discusses Hidden Markov Models, laying part of foundation for the following chapters, is full of mathematical formulas that won't be easy to follow unless you already have some background on the topic. I would recommend that instead you read L. Rabiner's paper "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition". Rabiner not only shows the formulas, he describes their meaning, and the tutorial makes it easy for you to follow the text and actually understand what is going on.
That said, every chapter includes a section on additional reading (the above paper is mentioned in chapter 2) so you can always look up the references to help you understand the material, if you need to.
To summarize, this is an excellent text, that I would recommend to experts in the field, but beginners may need additional reading to get a better understanding of the book.
Summary of Statistical Methods for Speech Recognition (Language, Speech, and Communication)
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.Speech Processing Books
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