- Series: Language, Speech, and Communication
- Hardcover: 305 pages
- Publisher: A Bradford Book; Fourth Printing edition (January 16, 1998)
- Language: English
- ISBN-10: 0262100665
- ISBN-13: 978-0262100663
- Product Dimensions: 6.2 x 1 x 9 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 11 customer reviews
- Amazon Best Sellers Rank: #1,394,651 in Books (See Top 100 in Books)
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Statistical Methods for Speech Recognition (Language, Speech, and Communication) Fourth Printing Edition
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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)
About the Author
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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Top customer reviews
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 introduction to the field as a whole, i'd suggest 'Fundamentals of Speech Recognition' by Rabiner & Juang, and if it's code that you're looking for, there's lots of excellent open source stuff available on the net, notably from CMU and Cambridge, and there are some recent books in the market exclusively devoted to implementation of speech recognition systems.
To sum up, if you have some exposure to speech recognition and want to learn the maths & concepts behind the Statistical approach to Speech Recognition, this is your book.
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.
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. He covers several auxiliary topics including information theory and perplexity, the spelling to phoneme mapping, and the use of triphones for cross-phoneme modeling. Each chapter is a worthy introduction to an important topic.
This book does not presuppose much in the way of mathematical, computational, or linguistic background. A simple intro to probability and some experience with search problems would be of help, but isn't necessary -- you'll learn a lot about these topics reading the book.
All in all, this is the best thorough introduction to speech recognition that you can find. Read it along with Manning and Schuetze's "Foundations of Statistical Natural Language Processing" from the same series; there's a little overlap in language modeling, but not much. You might want to start with the gentler book by Jurafsky and Martin, "Speech and Language Processing", before tackling either Jelinek or Manning and Schuetze.