Language identification using spectral and prosodic features [electronic resource]
- 作者： Rao, K. Sreenivasa.
- 其他作者：
- 其他題名：
- SpringerBriefs in electrical and computer engineering
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： SpringerBriefs in electrical and computer engineering
- 主題： Linguistic analysis (Linguistics) , Engineering , Signal, Image and Speech Processing. , Language Translation and Linguistics. , Computational linguistics , India--Languages--Prosodic analysis. , India--Languages--Spectral analysis.
- ISBN： 9783319171630 (electronic bk.) 、 9783319171623 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 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)
- 摘要註: 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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讀者標籤：
- 系統號： 005132812 | 機讀編目格式
館藏資訊
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.