Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection

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dc.authorid0000-0002-2126-8757
dc.contributor.authorAltun, Halis
dc.contributor.authorPolat, Goekhan
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractThis paper deals with the strategies for feature selection and multi-class classification in the emotion detection problem. The aim is two-fold: to increase the effectiveness of four feature selection algorithms and to improve accuracy of multi-class Classifiers for emotion detection problem under different frameworks and strategies. Although, a large amount of research has been conducted to determine the most informative features in emotion detection, it is still an open problem to identify reliably discriminating features. As it is believed that highly informative features are more critical factor than classifier itself, recent Studies have been focused oil identifying the features that contribute more to the classification problem. In this paper, in order to improve the performance of multi-class SVMs in emotion detection, 58 features extracted from recorded speech samples are processed in two new frameworks to boost the feature selection algorithms. Evaluation of the final feature sets validates that the frameworks are able to select more informative Subset of the features in terms of class-separability. Also it is found that among four feature selection algorithms, a recently proposed one, LSBOUND, significantly outperforms the others. The accuracy rate obtained in the proposed framework is the highest achievement reported so far in the literature for the same dataset. (C) 2008 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipTUBITAK Project [104EI79]
dc.description.sponsorshipThis work has been sponsored by TUBITAK Project under the contract of 104EI79. Corresponding author also Would like to thank Prof. Dr. J Shawe-Taylor for his hospitality and guidance during the academic visit in the Summer of 2006 granted by TUBITAK at University Of Southampton and at University College of London. We would also like to thank Dr. S. Szedmak for providing the MMR algorithm.
dc.identifier.doi10.1016/j.eswa.2008.10.005
dc.identifier.endpage8203
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4
dc.identifier.scopus2-s2.0-60249092335
dc.identifier.scopusqualityQ1
dc.identifier.startpage8197
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2008.10.005
dc.identifier.urihttps://hdl.handle.net/11480/5062
dc.identifier.volume36
dc.identifier.wosWOS:000264528600102
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEmotion detection
dc.subjectFeature selection
dc.subjectSpeech analysis
dc.subjectMachine learning
dc.titleBoosting selection of speech related features to improve performance of multi-class SVMs in emotion detection
dc.typeArticle

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