A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal

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dc.authorid0000-0002-8929-3473en_US
dc.authorid0000-0001-8699-375Xen_US
dc.authorid0000-0001-7466-0368en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-2064-7157
dc.contributor.authorYogesh, Chinnakalai K.
dc.contributor.authorHariharan, Muthusamy
dc.contributor.authorNgadiran, Ruzelita
dc.contributor.authorAdom, Abdul Hamid
dc.contributor.authorYaacob, Sazali
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:48:58Z
dc.date.available2021-06-23T19:48:58Z
dc.date.issued2017
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractSpeech signals and glottal signals convey speakers' emotional state along with linguistic information. To recognize speakers' emotions and respond to it expressively is very much important for human-machine interaction. To develop a subject independent speech emotion/stress recognition system, by identifying speaker's emotion from their voices, features from OpenSmile toolbox, higher order spectral features and feature selection algorithm, is proposed in this work. Feature selection plays an important role in overcoming the challenge of dimensionality in several applications. This paper proposes a new particle swarm optimization assisted Biogeography-based algorithm for feature selection. The simulations were conducted using Berlin Emotional Speech Database (BES), Surrey Audio-Visual Expressed Emotion Database (SAVEE), Speech under Simulated and Actual Stress (SUSAS) and also validated using eight benchmark datasets. These datasets are of different dimensions and classes. Totally eight different experiments were conducted and obtained the recognition rates in range of 90.31%-99.47% (BES database), 62.50%-78.44% (SAVEE database) and 85.83%-98.70% (SUSAS database). The obtained results convincingly prove the effectiveness of the proposed feature selection algorithm when compared to the previous works and other metaheuristic algorithms (BBO and PSO). (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2016.10.035
dc.identifier.endpage158en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-84994056104en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage149en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2016.10.035
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9292
dc.identifier.volume69en_US
dc.identifier.wosWOS:000389111000014en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpeech Signalsen_US
dc.subjectEmotionsen_US
dc.subjectFeature Extractionen_US
dc.subjectFeature Selection and Emotion Recognitionen_US
dc.titleA new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signalen_US
dc.typeArticleen_US

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