A modified feature selection method based on metaheuristic algorithms for speech emotion recognition

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dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.authoridYILDIRIM, Serdar/0000-0003-3151-9916
dc.authoridKilic, Fatih/0000-0002-8550-1562
dc.contributor.authorYildirim, Serdar
dc.contributor.authorKaya, Yasin
dc.contributor.authorKılıç, Fatih
dc.date.accessioned2025-01-06T17:37:51Z
dc.date.available2025-01-06T17:37:51Z
dc.date.issued2021
dc.description.abstractFeature selection plays an important role to build a successful speech emotion recognition system. In this paper, a feature selection approach which modifies the initial population generation stage of metaheuristic search algorithms, is proposed. The approach is evaluated on two metaheuristic search algorithms, a nondominated sorting genetic algorithm-II (NSGA-II) and Cuckoo Search in the context of speech emotion recognition using Berlin emotional speech database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) database. Results show that the presented feature selection algorithms reduce the number of features significantly and are still effective for emotion classification from speech. Specifically, in speaker-dependent experiments of the EMO-DB, recognition rates of 87.66% and 87.20% are obtained using selected features by modified Cuckoo Search and NSGA-II respectively, whereas, for the IEMOCAP database, the accuracies of 69.30% and 68.32% are obtained using SVM classifier. For the speaker-independent experiments, we achieved comparable results for both databases. Specifically, recognition rates of 76.80% and 76.82% for EMO-DB and 59.37% and 59.52% for IEMOCAP using modified NSGA-II and Cuckoo Search respectively. (C) 2020 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.apacoust.2020.107721
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-85094165534
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2020.107721
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2390
dc.identifier.volume173
dc.identifier.wosWOS:000595628100035
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofApplied Acoustics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFeature selection
dc.subjectEmotion recognition from speech
dc.subjectMetaheuristic search algorithms
dc.titleA modified feature selection method based on metaheuristic algorithms for speech emotion recognition
dc.typeArticle

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