Determining efficiency of speech feature groups in emotion detection [Ses özni·teli·k gruplarinin duygu tespi·ti·nde etki·nli·kleri·ni·n beli·rlenmesi·]

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dc.contributor.authorPolat G.
dc.contributor.authorAltun H.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2007
dc.departmentNiğde ÖHÜ
dc.description2007 IEEE 15th Signal Processing and Communications Applications, SIU -- 11 June 2007 through 13 June 2007 -- Eskisehir -- 73089
dc.description.abstractFeatures, extract from speech parameter are frequently used in emotion detection problem. Prosodic, MFCC, LPC and band energy feature groups are commonly used in literature to detect emotion in speech. The aim of the study is to examine the efficiency of these features groups in emotion detection problem using a SVM classifier.
dc.identifier.doi10.1109/SIU.2007.4298582
dc.identifier.isbn1424407192 -- 9781424407194
dc.identifier.scopus2-s2.0-50249117767
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1109/SIU.2007.4298582
dc.identifier.urihttps://hdl.handle.net/11480/1175
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isotr
dc.relation.ispartof2007 IEEE 15th Signal Processing and Communications Applications, SIU
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleDetermining efficiency of speech feature groups in emotion detection [Ses özni·teli·k gruplarinin duygu tespi·ti·nde etki·nli·kleri·ni·n beli·rlenmesi·]
dc.typeConference Object

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