Speech emotion recognition using spectral features

In order to improve the performance of the speech emotion recognition system and reduce the computing complexity, a speech emo- tion recognition based on optimized coefficients number for spectral fea- tures is proposed. Experimental studies are performed over the Berlin emotional Database, using su...

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Main Authors:	Salam, Md. Sah, Mohamed Idris, Inshirah Abdelraman
Format:	Conference or Workshop Item
Published:	2015
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/61621/ http://www.globaleventslist.elsevier.com/events/2015/12/machine-learning-and-signal-processing-international-conference/
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Institution:	Universiti Teknologi Malaysia

id	my.utm.61621
record_format	eprints
spelling	my.utm.616212017-04-25T06:53:39Z http://eprints.utm.my/id/eprint/61621/ Speech emotion recognition using spectral features Salam, Md. Sah Mohamed Idris, Inshirah Abdelraman QA75 Electronic computers. Computer science In order to improve the performance of the speech emotion recognition system and reduce the computing complexity, a speech emo- tion recognition based on optimized coefficients number for spectral fea- tures is proposed. Experimental studies are performed over the Berlin emotional Database, using support vector machine (SVM) classifier and five spectral features MFCC, LPC, LPCC, PLP, and PLP-RASTA. The experiment result shows that the speech emotion recognition based on coefficients number can improve the performance of the emotion recog- nition system effectively. abstract environment. 2015 Conference or Workshop Item PeerReviewed Salam, Md. Sah and Mohamed Idris, Inshirah Abdelraman (2015) Speech emotion recognition using spectral features. In: International Conference on Machine Learning and Signal Processing MALSIP 2015, 15-17 Dec, 2015, Vietnam. http://www.globaleventslist.elsevier.com/events/2015/12/machine-learning-and-signal-processing-international-conference/
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Salam, Md. Sah Mohamed Idris, Inshirah Abdelraman Speech emotion recognition using spectral features
description	In order to improve the performance of the speech emotion recognition system and reduce the computing complexity, a speech emo- tion recognition based on optimized coefficients number for spectral fea- tures is proposed. Experimental studies are performed over the Berlin emotional Database, using support vector machine (SVM) classifier and five spectral features MFCC, LPC, LPCC, PLP, and PLP-RASTA. The experiment result shows that the speech emotion recognition based on coefficients number can improve the performance of the emotion recog- nition system effectively. abstract environment.
format	Conference or Workshop Item
author	Salam, Md. Sah Mohamed Idris, Inshirah Abdelraman
author_facet	Salam, Md. Sah Mohamed Idris, Inshirah Abdelraman
author_sort	Salam, Md. Sah
title	Speech emotion recognition using spectral features
title_short	Speech emotion recognition using spectral features
title_full	Speech emotion recognition using spectral features
title_fullStr	Speech emotion recognition using spectral features
title_full_unstemmed	Speech emotion recognition using spectral features
title_sort	speech emotion recognition using spectral features
publishDate	2015
url	http://eprints.utm.my/id/eprint/61621/ http://www.globaleventslist.elsevier.com/events/2015/12/machine-learning-and-signal-processing-international-conference/
_version_	1643655221227814912

Speech emotion recognition using spectral features

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