Statistical Methods for Speech Recognition Frederick Jelinek

By:

Jelinek, Frederick

Material type: Text

TextLanguage: English Publication details: MIT Press 1997 Cambridge, MassEdition: 1st EdDescription: 283p. 15.88 x 2.24 x 23.5 cmISBN:

9780262100663

Subject(s):

DDC classification:

006.454 JEL

Summary: 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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Holdings
Item type	Current library	Call number	Materials specified	Status	Date due	Barcode
Books	Rashtriya Raksha University	006.454 JEL (Browse shelf(Opens below))		Available		10096

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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