Language Identification Using Spectral and Prosodic Features
1st Edition
Published on 31. March 2015
XI, 98 pages
978-3-319-17163-0 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 98 p. 21 illus., 5 illus. in color.
File size
2,26 MB
ISBN-13
978-3-319-17163-0 (9783319171630)
DOI
10.1007/978-3-319-17163-0
Schweitzer Classification
Other editions
Additional editions
K. Sreenivasa Rao | V. Ramu Reddy | Sudhamay Maity
Language Identification Using Spectral and Prosodic Features
Book
04/2015
Springer
€53.49
Shipment within 10-15 days
Content
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).
