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Speech recognition using articulator...

Rao, K. Sreenivasa.

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Speech recognition using articulatory and excitation source features

Record Type:	Electronic resources : 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.
[NT 15003449]:	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.
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:

753709
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 : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
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 753709
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Language Translation and Linguistics. $3 892561
650 2 4 $a Computational Linguistics. $3 893900
700 0 $a Manjunath K.E. $3 3220566
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in electrical and computer engineering. $3 1565565
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-49220-9
950 $a Engineering (Springer-11647)