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150402 ||| eng |
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|a 9783319171630
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|a Rao, K. Sreenivasa
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| 245 |
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|a Language Identification Using Spectral and Prosodic Features
|h Elektronische Ressource
|c by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity
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| 250 |
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|a 1st ed. 2015
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2015, 2015
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| 300 |
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|a XI, 98 p. 21 illus., 5 illus. in color
|b online resource
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| 505 |
0 |
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|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)
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| 653 |
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|a Computational Linguistics
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| 653 |
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|a Computational linguistics
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Signal processing
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| 653 |
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|a Natural language processing (Computer science)
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| 700 |
1 |
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|a Reddy, V. Ramu
|e [author]
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| 700 |
1 |
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|a Maity, Sudhamay
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-17163-0
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-17163-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 621.382
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| 520 |
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|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
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