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...
| Published in: | Springer eBooks |
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| Main Authors: | , , |
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Series: | SpringerBriefs in Electrical and Computer Engineering,
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1007/978-3-319-17163-0 Перейти в каталог НБ ТГУ |
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| 039 | 9 | |y 201702122238 |z Александр Эльверович Гилязов | |
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| 100 | 1 | |a Rao, K. Sreenivasa. |e author. |9 448928 | |
| 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. |9 742221 | ||
| 300 | |a XI, 98 p. 21 illus., 5 illus. in color. |b online resource. | ||
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| 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 engineering. |9 224332 | |
| 650 | 0 | |a Computational linguistics. |9 309798 | |
| 650 | 1 | 4 | |a Engineering. |9 224332 |
| 650 | 2 | 4 | |a Signal, Image and Speech Processing. |9 274103 |
| 650 | 2 | 4 | |a Language Translation and Linguistics. |9 304148 |
| 650 | 2 | 4 | |a Computational Linguistics. |9 309798 |
| 700 | 1 | |a Reddy, V. Ramu. |e author. |9 465377 | |
| 700 | 1 | |a Maity, Sudhamay. |e author. |9 465378 | |
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