Language Identification Using Spectral and Prosodic Features

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 stres...

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Main Authors: Rao, K. Sreenivasa, Reddy, V. Ramu (Author), Maity, Sudhamay (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2015, 2015
Edition:1st ed. 2015
Series:SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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100 1 |a Rao, K. Sreenivasa 
245 0 0 |a Language Identification Using Spectral and Prosodic Features  |h Elektronische Ressource  |c by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity 
250 |a 1st ed. 2015 
260 |a Cham  |b Springer International Publishing  |c 2015, 2015 
300 |a XI, 98 p. 21 illus., 5 illus. in color  |b online resource 
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) 
653 |a Signal, Image and Speech Processing 
653 |a Image processing 
653 |a Computational linguistics 
653 |a Computational Linguistics 
653 |a Speech processing systems 
653 |a Signal processing 
653 |a Natural Language Processing (NLP) 
653 |a Natural language processing (Computer science) 
700 1 |a Reddy, V. Ramu  |e [author] 
700 1 |a Maity, Sudhamay  |e [author] 
710 2 |a SpringerLink (Online service) 
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490 0 |a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning 
856 |u https://doi.org/10.1007/978-3-319-17163-0?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 621.382 
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