Keshet et al. (eds) - Automatic Speech and Speaker Recognition (Wiley, eBooki, AI, technologia mowy

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Automatic Speech and Speaker Recognition
Au tomatic Speech and Speaker R ecognition: Large Margin and Ker n el Methods
Edited by Joseph Keshet
and Samy Bengio © 2009 John Wiley & Sons, Ltd. ISBN: 978-0-470-69683-5
Automatic Speech and
Speaker Recognition
Large Margin and Kernel Methods
Joseph Keshet
IDIAP Research Institute, Martigny, Switzerland
Samy Bengio
Google Inc., Mountain View, CA, USA
A John Wiley and Sons, Ltd, Publication
This edition first published 2009
c
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Library of Congress Cataloging-in-Publication Data
Automatic speech and speaker recognition : large margin and kernel methods /
edited by Joseph Keshet, Samy Bengio.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-470-69683-5 (cloth)
1. Automatic speech recognition. I. Keshet, Joseph. II. Bengio, Samy.
TK7895.S65A983 2009
006.4’54–dc22
2008038551
A catalogue record for this book is available from the British Library.
ISBN 9780470696835 (H/B)
Set in 10/12pt Times by Sunrise Setting Ltd, Torquay, UK.
Printed in Singapore by Markono Print Media Pte Ltd.
Contents
List of Contributors
xi
Preface
xv
I Foundations
1
1
Introduction
3
Samy Bengio and Joseph Keshet
1.1
The Traditional Approach to Speech Processing . ...............
3
1.2
Potential Problems of the Probabilistic Approach
...............
5
1.3
Support Vector Machines for Binary Classification
. . . . . . ........
7
1.4
Outline
. . . ..................................
8
References.......................................
9
2 Theory and Practice of Support Vector Machines Optimization
11
Shai Shalev-Shwartz and Nathan Srebo
2.1 Introduction . .................................. 11
2.2 SVM and
L
2
-regularizedLinearPrediction .................. 12
2.2.1 BinaryClassificationandtheTraditionalSVM ............ 12
2.2.2 MoreGeneralLossFunctions ..................... 13
2.2.3 Examples................................ 13
2.2.4 Kernels ................................. 14
2.2.5 IncorporatingaBiasTerm ....................... 15
2.3 OptimizationAccuracyFromaMachineLearningPerspective ........ 16
2.4 StochasticGradientDescent .......................... 18
2.4.1 Sub-gradientCalculus ......................... 20
2.4.2 Rate of Convergence and Stopping Criteria . . . . . . ........ 21
2.5 Dual Decomposition Methods . . ....................... 22
2.5.1 Duality . . . . . . ........................... 23
2.6 Summary .................................... 25
References....................................... 26
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