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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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2015
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-17163-0 http://cds.cern.ch/record/2005811 |
_version_ | 1780946223968026624 |
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author | Rao, K Sreenivasa Reddy, V Ramu Maity, Sudhamay |
author_facet | Rao, K Sreenivasa Reddy, V Ramu Maity, Sudhamay |
author_sort | Rao, K Sreenivasa |
collection | CERN |
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. |
id | cern-2005811 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2015 |
publisher | Springer |
record_format | invenio |
spelling | cern-20058112021-04-21T20:24:29Zdoi:10.1007/978-3-319-17163-0http://cds.cern.ch/record/2005811engRao, K SreenivasaReddy, V RamuMaity, SudhamayLanguage identification using spectral and prosodic featuresEngineeringThis 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.Springeroai:cds.cern.ch:20058112015 |
spellingShingle | Engineering Rao, K Sreenivasa Reddy, V Ramu Maity, Sudhamay Language identification using spectral and prosodic features |
title | Language identification using spectral and prosodic features |
title_full | Language identification using spectral and prosodic features |
title_fullStr | Language identification using spectral and prosodic features |
title_full_unstemmed | Language identification using spectral and prosodic features |
title_short | Language identification using spectral and prosodic features |
title_sort | language identification using spectral and prosodic features |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-3-319-17163-0 http://cds.cern.ch/record/2005811 |
work_keys_str_mv | AT raoksreenivasa languageidentificationusingspectralandprosodicfeatures AT reddyvramu languageidentificationusingspectralandprosodicfeatures AT maitysudhamay languageidentificationusingspectralandprosodicfeatures |