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Speech recognition using articulatory and excitation source features

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Speech recognition using articulatory and excitation source features/ by K. Sreenivasa Rao, Manjunath K.E.
Author:	Rao, K. Sreenivasa.
other author:	Manjunath K.E.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xi, 92 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Automatic speech recognition. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-49220-9
ISBN:	9783319492209

Speech recognition using articulatory and excitation source features
Rao, K. Sreenivasa.

Speech recognition using articulatory and excitation source features[electronic resource] /by K. Sreenivasa Rao, Manjunath K.E. - Cham :Springer International Publishing :2017. - xi, 92 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Literature Review -- Articulatory Features for Phone Recognition -- Excitation Source Features for Phone Recognition -- Articulatory and Excitation Source Features for Speech Recognition in Read, Extempore and Conversation Modes -- Conclusion -- Appendix A: MFCC Features -- Appendix B: Pattern Recognition Models.

This book discusses the contribution of articulatory and excitation source information in discriminating sound units. The authors focus on excitation source component of speech -- and the dynamics of various articulators during speech production -- for enhancement of speech recognition (SR) performance. Speech recognition is analyzed for read, extempore, and conversation modes of speech. Five groups of articulatory features (AFs) are explored for speech recognition, in addition to conventional spectral features. Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features. The authors close by discussing various combinations of spectral, articulatory and source features, and the desired models to enhance the performance of SR systems.

ISBN: 9783319492209

Standard No.: 10.1007/978-3-319-49220-9doiSubjects--Topical Terms:

557847
Automatic speech recognition.

LC Class. No.: TK7895.S65

Dewey Class. No.: 006.454

Speech recognition using articulatory and excitation source features
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245 1 0 $a Speech recognition using articulatory and excitation source features $h [electronic resource] / $c by K. Sreenivasa Rao, Manjunath K.E.
260 $a Cham : $c 2017. $b Springer International Publishing : $b Imprint: Springer,
300 $a xi, 92 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering, $x 2191-8112
505 0 $a Introduction -- Literature Review -- Articulatory Features for Phone Recognition -- Excitation Source Features for Phone Recognition -- Articulatory and Excitation Source Features for Speech Recognition in Read, Extempore and Conversation Modes -- Conclusion -- Appendix A: MFCC Features -- Appendix B: Pattern Recognition Models.
520 $a This book discusses the contribution of articulatory and excitation source information in discriminating sound units. The authors focus on excitation source component of speech -- and the dynamics of various articulators during speech production -- for enhancement of speech recognition (SR) performance. Speech recognition is analyzed for read, extempore, and conversation modes of speech. Five groups of articulatory features (AFs) are explored for speech recognition, in addition to conventional spectral features. Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features. The authors close by discussing various combinations of spectral, articulatory and source features, and the desired models to enhance the performance of SR systems.
650 0 $a Automatic speech recognition. $3 557847
650 1 4 $a Engineering. $3 561152
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Language Translation and Linguistics. $3 670050
650 2 4 $a Computational Linguistics. $3 670080
700 0 $a Manjunath K.E. $3 1250527
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830 0 $a SpringerBriefs in electrical and computer engineering. $3 883975
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950 $a Engineering (Springer-11647)