Statistical Methods for Speech Recognition

Frederick Jelinek

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ISBN 10: 0262546604 ISBN 13: 9780262546607
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Statistical Methods for Speech Recognition | Frederick Jelinek | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2022 | MIT Press | EAN 9780262546607 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 125764940

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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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About the Author: Frederick Jelinek is Julian Sinclair Smith Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, where he is also Director for the Center for Language and Speech Processing.

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Title: Statistical Methods for Speech Recognition
Publisher: MIT Press
Publication Date: 2022
Binding: Taschenbuch
Condition: Neu

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Frederick Jelinek is Julian Sinclair Smith Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, where he is also Director for the Center for Language and Speech Processing.This book reflects deca. Seller Inventory # 749802968

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.Bradford Books imprint. Seller Inventory # 9780262546607

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