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Binding: Hardcover
Dewey Decimal Number: 006.454
EAN: 9780262100663
ISBN: 0262100665
Label: The MIT Press
Manufacturer: The MIT Press
Number Of Items: 1
Number Of Pages: 305
Publication Date: January 16, 1998
Publisher: The MIT Press
Studio: The MIT Press
Alternate Versions: Click to Display
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"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
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.
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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 ... Read More
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This book provides a comprehensive introduction to the statistical models and algorithms used for speech recognition. Jelinek sets up the speech recognition problem in the traditional way as the decoding half of Shannon's noisy channel model. While Jelinek glosses over signal processing, he provides an excellent overview of the symbolic stages of processing involved in speech recognition.
After a quick introduction, Jelinek digs into the statistics behind Hidden Markov Models (HMMs), the foundation of almost all of today's speech recognizers. This is followed by chapters devoted to acoustic modeling (probability of acoustics given words) and language modeling (probability of a given sequence of words), and the algorithmic search induced by this model. There are also advanced chapters on fast match (widely used heuristics for pruning search), the Expectation-Maximization (EM) algorithm for training, and the use of decision trees, maximum entropy and backoff for language models. ... Read More
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This book provides an excellent overview of speech recognition technology using Hidden Markov Models. Although Jelinek is clearly speaking with respect to his experience at IBM - he might as well be describing any other commercial speech recognition framework in the world. As a researcher and programmer in the area of speech recognition I regard this book as an excellent reference. It is concise, and I would say that anyone with a reasonable grasp of mathematics should have no trouble understanding most of the topics. In some of the more advanced areas some readers might need to refer to one of reference papers described in the book. I agree with the first reader - destined to be a classic!
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This book is possibly the first of its kind - exclusively devoted to Statistical Speech Recognition. The author is a pioneer in the area - one of the 'fathers' of the field,as it were. Thus one expects the text to be authoritative, and it is. The 'information density' is very high - it's a small book, but absolutely packed with information. You'll learn a lot about Hidden Markov Models and their use in Speech Recognition, but it also addresses many other issues, like language modelling and grammar, making it much more than a mere 'speech maths' book.
However, this is definitely not meant for absolute newcomers to the field of speech processing, and it does assume some background in advaced mathematics as well, especially in probability.
If you're looking for other aspects of Speech Recognition or code, you've come to the wrong place - but please don't spoil the rating of an excellent book by complaining that it doesn't have what it never promised to :-) - if you want a solid ... Read More
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This is an excellent book for people with speech recognition knowledge. The algorithms are very well described in a sound and comprehensive mathematical framework.
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