Detection and Analysis of Emotion from Speech Signals

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dc.contributor.authorDavletcharova, Assel
dc.contributor.authorSugathan, Sherin
dc.contributor.authorAbraham, Bibia
dc.contributor.authorJames, Alex Pappachen
dc.creatorAssel, Davletcharova
dc.date.accessioned2017-12-15T04:48:49Z
dc.date.available2017-12-15T04:48:49Z
dc.date.issued2015-01-01
dc.description.abstractAbstract Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.en_US
dc.identifierDOI:10.1016/j.procs.2015.08.032
dc.identifier.citationAssel Davletcharova, Sherin Sugathan, Bibia Abraham, Alex Pappachen James, Detection and Analysis of Emotion from Speech Signals, In Procedia Computer Science, Volume 58, 2015, Pages 91-96en_US
dc.identifier.issn18770509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050915021432
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/2928
dc.language.isoenen_US
dc.publisherProcedia Computer Scienceen_US
dc.relation.ispartofProcedia Computer Science
dc.rights.licenseCopyright © 2015 The Authors. Published by Elsevier B.V.
dc.subjectEmotion Analysisen_US
dc.subjectEmotion Classificationen_US
dc.subjectSpeech Processingen_US
dc.subjectMel-Frequency Cepstral Coefficientsen_US
dc.titleDetection and Analysis of Emotion from Speech Signalsen_US
dc.typeArticleen_US
elsevier.aggregationtypeJournal
elsevier.coverdate2015-01-01
elsevier.coverdisplaydate2015
elsevier.endingpage96
elsevier.identifier.doi10.1016/j.procs.2015.08.032
elsevier.identifier.eid1-s2.0-S1877050915021432
elsevier.identifier.piiS1877-0509(15)02143-2
elsevier.identifier.scopusid84970978774
elsevier.issue.nameSecond International Symposium on Computer Vision and the Internet (VisionNet’15)
elsevier.openaccess1
elsevier.openaccessarticletrue
elsevier.openaccessuserlicensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
elsevier.openarchivearticlefalse
elsevier.startingpage91
elsevier.teaserRecognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing...
elsevier.volume58
workflow.import.sourcescience

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