Language identification using spectral and prosodic features [electronic resource] / K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity.
2015
PF1529
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Title
Language identification using spectral and prosodic features [electronic resource] / K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity.
Author
Sreenivasa Rao, K., author.
ISBN
9783319171630 electronic book
3319171631 electronic book
9783319171623
3319171631 electronic book
9783319171623
Published
Cham : Springer, [2015]
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-319-17163-0 doi
Call Number
PF1529
Dewey Decimal Classification
491/.1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (viewed April 8, 2015).
Added Author
Reddy, V. Ramu, author.
Maity, Sudhamay, author.
Maity, Sudhamay, author.
Series
SpringerBriefs in electrical and computer engineering.
SpringerBriefs in speech technology.
SpringerBriefs in speech technology.
Available in Other Form
Print version: 9783319171623
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Table of Contents
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).
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).