Bispectral features and mean shift clustering for stress and emotion recognition from natural speech

dc.authorid0000-0002-8929-3473en_US
dc.authorid0000-0003-4526-0749en_US
dc.authorid0000-0001-7466-0368en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorC. K., Yogesh
dc.contributor.authorMuthusamy, Hariharan
dc.contributor.authorRajamanickam, Yuvaraj
dc.contributor.authorNgadiran, Ruzelita
dc.contributor.authorAdom, A. H.
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:45:36Z
dc.date.available2021-06-23T19:45:36Z
dc.date.issued2017
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractA new set of features and feature enhancement techniques are proposed to recognize emotion and stress from speech signal. The speech waveforms and the glottal waveforms (derived from the recorded emotional/stress speech waveforms) were processed by using third order statistics called bispectrum and 28 (14 from speech waveforms and 14 from glottal waveforms) bispectral based features. In this work, mean shift clustering was used to enhance the discrimination ability of the extracted Bispectral Features (BSFs). Four classifiers were used to distinguish different emotional and stressed states. The performance of the proposed method is tested with three databases. Different experiments were conducted and recognition rates were achieved in the range between 93.44% and 100% for Berlin emotional speech database (BES), between 73.81% and 97.23% for Surrey audio-visual expressed emotion database (SAVEE), between 93.8% and 100% for speech under simulated and actual stress simulated domain (SUSAS) (recognition of multi-style speech under stress-neutral, loud, lombard and anger) and 100% for SUSAS actual domain (recognition of three different levels of stress. high, medium and low). The obtained results indicate that the proposed bispectral based features and mean shift clustering provide promising results to recognize emotion and stress from speech signal. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compeleceng.2017.01.024
dc.identifier.endpage691en_US
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85010987097en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage676en_US
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2017.01.024
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9180
dc.identifier.volume62en_US
dc.identifier.wosWOS:000413881700051en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpeech Signalsen_US
dc.subjectGlottal Signalsen_US
dc.subjectEmotionsen_US
dc.subjectFeature Extraction and Emotion Recognitionen_US
dc.titleBispectral features and mean shift clustering for stress and emotion recognition from natural speechen_US
dc.typeArticleen_US

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