Efficient and effective strategies for cross-corpus acoustic emotion recognition

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dc.authorid0000-0003-3424-652X
dc.authorid0000-0001-7947-5508
dc.authorscopusid36241785000
dc.authorscopusid57219469958
dc.authorwosidKarpov, Alexey A/A-8905-2012
dc.authorwosidKAYA, Heysem/V-4493-2019
dc.contributor.authorKaya, Heysem
dc.contributor.authorKarpov, Alexey A.
dc.date.accessioned2022-05-11T14:15:53Z
dc.date.available2022-05-11T14:15:53Z
dc.date.issued2018
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAn important research direction in speech technology is robust cross-corpus and cross-language emotion recognition. In this paper, we propose computationally efficient and performance effective feature normalization strategies for the challenging task of cross-corpus acoustic emotion recognition. We particularly deploy a cascaded normalization approach, combining linear speaker level, nonlinear value level and feature vector level normalization to minimize speaker-and corpus-related effects as well as to maximize class separability with linear kernel classifiers. We use extreme learning machine classifiers on five corpora representing five languages from different families, namely Danish, English, German, Russian and Turkish. Using a standard set of suprasegmental features, the proposed normalization strategies show superior performance compared to benchmark normalization approaches commonly used in the literature. (C) 2017 Elsevier B.V. All rights reserved.
dc.description.sponsorshipRussian Foundation for Basic ResearchRussian Foundation for Basic Research (RFBR) [16-37-60100]; Council for Grants of the President of Russia [MD-254.2017.8]
dc.description.sponsorshipThis research is partially supported by the Russian Foundation for Basic Research (project No 16-37-60100) and by the Council for Grants of the President of Russia (project No MD-254.2017.8).
dc.identifier.doi10.1016/j.neucom.2017.09.049
dc.identifier.endpage1034
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.scopus2-s2.0-85030651372
dc.identifier.scopusqualityQ1
dc.identifier.startpage1028
dc.identifier.urihttps://doi.org/10.1016/j.neucom.2017.09.049
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6106
dc.identifier.volume275
dc.identifier.wosWOS:000418370200098
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKaya, Heysem
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofNeurocomputing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectExtreme learning machines
dc.subjectAcoustic emotion recognition
dc.subjectCross-corpus adaptation
dc.subjectExtreme Learning-Machine
dc.subjectPhysical Load
dc.subjectChallenge
dc.subjectClassification
dc.subjectNetworks
dc.titleEfficient and effective strategies for cross-corpus acoustic emotion recognition
dc.typeArticle

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