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Language identification using spectr...

Rao, K. Sreenivasa.

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Language identification using spectral and prosodic features

Record Type:	Electronic resources : Monograph/item
Title/Author:	Language identification using spectral and prosodic features/ by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity.
Author:	Rao, K. Sreenivasa.
other author:	Reddy, V. Ramu.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xi, 98 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Literature Review -- Language Identification using Spectral Features -- Language Identification using Prosodic Features -- Summary and Conclusions -- Appendix A: LPCC Features -- Appendix B: MFCC Features -- Appendix C: Gaussian Mixture Model (GMM)
Contained By:	Springer eBooks
Subject:	Linguistic analysis (Linguistics) -
Subject:	India -
Online resource:	http://dx.doi.org/10.1007/978-3-319-17163-0
ISBN:	9783319171630 (electronic bk.)

Language identification using spectral and prosodic features
Rao, K. Sreenivasa.

Language identification using spectral and prosodic features[electronic resource] /by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity. - Cham :Springer International Publishing :2015. - xi, 98 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Literature Review -- Language Identification using Spectral Features -- Language Identification using Prosodic Features -- Summary and Conclusions -- Appendix A: LPCC Features -- Appendix B: MFCC Features -- Appendix C: Gaussian Mixture Model (GMM)

This book discusses the impact of spectral features extracted from frame level, glottal closure regions, and pitch-synchronous analysis on the performance of language identification systems. In addition to spectral features, the authors explore prosodic features such as intonation, rhythm, and stress features for discriminating the languages. They present how the proposed spectral and prosodic features capture the language specific information from two complementary aspects, showing how the development of language identification (LID) system using the combination of spectral and prosodic features will enhance the accuracy of identification as well as improve the robustness of the system. This book provides the methods to extract the spectral and prosodic features at various levels, and also suggests the appropriate models for developing robust LID systems according to specific spectral and prosodic features. Finally, the book discuss about various combinations of spectral and prosodic features, and the desired models to enhance the performance of LID systems.

ISBN: 9783319171630 (electronic bk.)

Standard No.: 10.1007/978-3-319-17163-0doiSubjects--Topical Terms:

598035
Linguistic analysis (Linguistics)
Subjects--Geographical Terms:

739969
India

LC Class. No.: PF1529

Dewey Class. No.: 491.1

Language identification using spectral and prosodic features
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035 $a 978-3-319-17163-0
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041 0 $a eng
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072 7 $a COM073000 $2 bisacsh
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090 $a PF1529 $b .R215 2015
100 1 $a Rao, K. Sreenivasa. $3 2058221
245 1 0 $a Language identification using spectral and prosodic features $h [electronic resource] / $c by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xi, 98 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 -- Language Identification using Spectral Features -- Language Identification using Prosodic Features -- Summary and Conclusions -- Appendix A: LPCC Features -- Appendix B: MFCC Features -- Appendix C: Gaussian Mixture Model (GMM)
520 $a This book discusses the impact of spectral features extracted from frame level, glottal closure regions, and pitch-synchronous analysis on the performance of language identification systems. In addition to spectral features, the authors explore prosodic features such as intonation, rhythm, and stress features for discriminating the languages. They present how the proposed spectral and prosodic features capture the language specific information from two complementary aspects, showing how the development of language identification (LID) system using the combination of spectral and prosodic features will enhance the accuracy of identification as well as improve the robustness of the system. This book provides the methods to extract the spectral and prosodic features at various levels, and also suggests the appropriate models for developing robust LID systems according to specific spectral and prosodic features. Finally, the book discuss about various combinations of spectral and prosodic features, and the desired models to enhance the performance of LID systems.
650 0 $a Linguistic analysis (Linguistics) $3 598035
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
651 0 $a India $ xHistorical geography. $3 739969
700 1 $a Reddy, V. Ramu. $3 2139419
700 1 $a Maity, Sudhamay. $3 2139420
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/10.1007/978-3-319-17163-0
950 $a Engineering (Springer-11647)