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Statistical Methods for Speech Recognition [Language, Speech, and Communication]

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Item specifics

Condition
Acceptable: A book with obvious wear. May have some damage to the cover but integrity still intact. ...
ISBN
9780262100663
Subject Area
Mathematics, Computers, Language Arts & Disciplines
Publication Name
Statistical Methods for Speech Recognition
Publisher
MIT Press
Item Length
9.2 in
Subject
Intelligence (Ai) & Semantics, Probability & Statistics / General, Linguistics / General, Computer Vision & Pattern Recognition, Speech & Audio Processing
Publication Year
1998
Series
Language, Speech, and Communication Ser.
Type
Textbook
Format
Hardcover
Language
English
Item Height
0.9 in
Author
Frederick Jelinek
Item Weight
20.2 Oz
Item Width
6.2 in
Number of Pages
305 Pages

About this product

Product Identifiers

Publisher
MIT Press
ISBN-10
0262100665
ISBN-13
9780262100663
eBay Product ID (ePID)
216311

Product Key Features

Number of Pages
305 Pages
Publication Name
Statistical Methods for Speech Recognition
Language
English
Publication Year
1998
Subject
Intelligence (Ai) & Semantics, Probability & Statistics / General, Linguistics / General, Computer Vision & Pattern Recognition, Speech & Audio Processing
Type
Textbook
Subject Area
Mathematics, Computers, Language Arts & Disciplines
Author
Frederick Jelinek
Series
Language, Speech, and Communication Ser.
Format
Hardcover

Dimensions

Item Height
0.9 in
Item Weight
20.2 Oz
Item Length
9.2 in
Item Width
6.2 in

Additional Product Features

Intended Audience
Trade
LCCN
97-034860
Dewey Edition
21
Reviews
"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
Grade From
College Graduate Student
Illustrated
Yes
Dewey Decimal
006.4/54
Synopsis
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.
LC Classification Number
TK7895.S65J45 1998

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