Please use this identifier to cite or link to this item: http://earsiv.odu.edu.tr:8080/xmlui/handle/11489/4523
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dc.contributor.authorYucesoy, Ergun-
dc.contributor.authorNabiyev, Vasif V.-
dc.date.accessioned2024-03-15T11:23:47Z-
dc.date.available2024-03-15T11:23:47Z-
dc.date.issued2014-
dc.identifier.citationYücesoy, E., Nabiyev, VV. (2014). Comparison of MFCC, LPCC and PLP Features for The Determination of a Speaker's Gender. , 321-324en_US
dc.identifier.isbn978-1-4799-4874-1-
dc.identifier.issn2165-0608-
dc.identifier.urihttps://www.webofscience.com/wos/woscc/full-record/WOS:000356351400060-
dc.identifier.urihttp://earsiv.odu.edu.tr:8080/xmlui/handle/11489/4523-
dc.descriptionWoS Categories: Engineering, Electrical & Electronic; Telecommunicationsen_US
dc.descriptionWeb of Science Index: Conference Proceedings Citation Index - Science (CPCI-S)en_US
dc.descriptionResearch Areas: Engineering; Telecommunicationsen_US
dc.descriptionConference Title: 22nd IEEE Signal Processing and Communications Applications Conference (SIU)en_US
dc.description.abstractGender information is a distinctive and the most important property in a speech. Determination of this information from a speech signal is a substantial subject. Gender information used for various purposes in many applications, provides the less error rate by defining the gender-dependent speech/speaker models. In this study, a system determining the gender of a speaker with no dependency from a text is proposed. In the proposed system, speech records represented by Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC) and Perceptual Linear Prediction Coefficients (PLP) features are classified according to the genders by using GMM model. In the study, the effect of feature type and its dimension and the number of GMM components on the success are comparatively investigated. In the experiments, the best result is obtained as 99.37% with 16-coefficient MFCC and 8-component GMM.en_US
dc.language.isoengen_US
dc.publisherIEEE-NEW YORKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGender identification, Gaussian Mixture Model (GMM), MFCC, LPCC, PLPen_US
dc.titleComparison of MFCC, LPCC and PLP Features for The Determination of a Speaker's Genderen_US
dc.typearticleen_US
dc.relation.journal2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)en_US
dc.contributor.departmentOrdu Üniversitesien_US
dc.contributor.authorID0000-0003-1707-384Xen_US
dc.identifier.startpage321en_US
dc.identifier.endpage324en_US
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