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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...

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Detalles Bibliográficos
Autores principales: Rao, K Sreenivasa, Koolagudi, Shashidhar G
Lenguaje:eng
Publicado: Springer 2013
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-1-4614-6360-3
http://cds.cern.ch/record/1518671
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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.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
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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