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Robust emotion recognition using spectral and prosodic features
In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complement...
Autores principales: | , |
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Lenguaje: | eng |
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Springer
2013
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-1-4614-6360-3 http://cds.cern.ch/record/1518671 |
_version_ | 1780928690071273472 |
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author | Rao, K Sreenivasa Koolagudi, Shashidhar G |
author_facet | Rao, K Sreenivasa Koolagudi, Shashidhar G |
author_sort | Rao, K Sreenivasa |
collection | CERN |
description | In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus. |
id | cern-1518671 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2013 |
publisher | Springer |
record_format | invenio |
spelling | cern-15186712021-04-21T23:11:07Zdoi:10.1007/978-1-4614-6360-3http://cds.cern.ch/record/1518671engRao, K SreenivasaKoolagudi, Shashidhar GRobust emotion recognition using spectral and prosodic featuresEngineeringIn this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.Springeroai:cds.cern.ch:15186712013 |
spellingShingle | Engineering Rao, K Sreenivasa Koolagudi, Shashidhar G Robust emotion recognition using spectral and prosodic features |
title | Robust emotion recognition using spectral and prosodic features |
title_full | Robust emotion recognition using spectral and prosodic features |
title_fullStr | Robust emotion recognition using spectral and prosodic features |
title_full_unstemmed | Robust emotion recognition using spectral and prosodic features |
title_short | Robust emotion recognition using spectral and prosodic features |
title_sort | robust emotion recognition using spectral and prosodic features |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-1-4614-6360-3 http://cds.cern.ch/record/1518671 |
work_keys_str_mv | AT raoksreenivasa robustemotionrecognitionusingspectralandprosodicfeatures AT koolagudishashidharg robustemotionrecognitionusingspectralandprosodicfeatures |