Speech Emotion Recognition using Unsupervised Feature Selection Algorithms

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dc.contributor.authorBandela, Surekha Reddy
dc.contributor.authorKumar, T. Kishore
dc.coverage.issue2cs
dc.coverage.volume29cs
dc.date.accessioned2020-10-09T10:59:15Z
dc.date.available2020-10-09T10:59:15Z
dc.date.issued2020-06cs
dc.description.abstractThe use of the combination of different speech features is a common practice to improve the accuracy of Speech Emotion Recognition (SER). Sometimes, this leads to an abrupt increase in the processing time and some of these features contribute less to emotion recognition often resulting in an incorrect prediction of emotion with which the accuracy of the SER system decreases substantially. Hence, there is a need to select the appropriate feature set that can contribute significantly to emotion recognition. This paper presents the use of Feature Selection with Adaptive Structure Learning (FSASL) and Unsupervised Feature Selection with Ordinal Locality (UFSOL) algorithms for feature dimension reduction. A novel Subset Feature Selection (SuFS) algorithm is proposed to further reduce the feature dimension and achieve a comparable better accuracy when used along with the FSASL and UFSOL algorithms. 1582 INTERSPEECH 2010 Paralinguistic, 20 Gammatone Cepstral Coefficients and Support Vector Machine classifier with 10-Fold Cross-Validation and Hold-Out Validation are considered in this work. The EMO-DB and IEMOCAP databases are used to evaluate the performance of the proposed SER system in terms of Classification accuracy and Computational Time. From the result analysis, it is evident that the proposed SER system outperforms the existing ones.en
dc.formattextcs
dc.format.extent353-364cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2020 vol. 29, č. 2, s. 353-364. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2020.0353en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/195194
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2020/20_02_0353_0364.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectSpeech Emotion Recognitionen
dc.subjectINTERSPEECH Paralinguistic Feature Seten
dc.subjectGTCCen
dc.subjectfeature selectionen
dc.subjectfeature optimizationen
dc.subjectFSASLen
dc.subjectUFSOLen
dc.subjectSuFSen
dc.titleSpeech Emotion Recognition using Unsupervised Feature Selection Algorithmsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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