Feature extraction and normalization in SVM speaker verification using telephone speech

Simple item page
dc.contributor.authorMazibuko, Thembisile Thulisileen_ZA
dc.date.accessioned2014-07-31T10:54:50Z
dc.date.available2014-07-31T10:54:50Z
dc.date.issued2007en_ZA
dc.descriptionIncludes bibliographical references (leaves 105-116).
dc.description.abstractIn this research the Support Vector Machine classifier is applied to a text independent speaker verification task using conversational telephone speech from the NIST 2000 Speaker Recognition Evaluation. The SVM is a discriminative classifier with good generalization characteristics. It has been shown to perform as well as, and sometimes outperform the more widely used Gaussian Mixture Model. The SVM, like other classifiers is vulnerable to environmental noise, distortions from transmission over communication channels such as the telephone channel, and intersession variability.en_ZA
dc.identifier.apacitationMazibuko, T. T. (2007). <i>Feature extraction and normalization in SVM speaker verification using telephone speech</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/5167en_ZA
dc.identifier.chicagocitationMazibuko, Thembisile Thulisile. <i>"Feature extraction and normalization in SVM speaker verification using telephone speech."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2007. http://hdl.handle.net/11427/5167en_ZA
dc.identifier.citationMazibuko, T. 2007. Feature extraction and normalization in SVM speaker verification using telephone speech. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Mazibuko, Thembisile Thulisile AB - In this research the Support Vector Machine classifier is applied to a text independent speaker verification task using conversational telephone speech from the NIST 2000 Speaker Recognition Evaluation. The SVM is a discriminative classifier with good generalization characteristics. It has been shown to perform as well as, and sometimes outperform the more widely used Gaussian Mixture Model. The SVM, like other classifiers is vulnerable to environmental noise, distortions from transmission over communication channels such as the telephone channel, and intersession variability. DA - 2007 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2007 T1 - Feature extraction and normalization in SVM speaker verification using telephone speech TI - Feature extraction and normalization in SVM speaker verification using telephone speech UR - http://hdl.handle.net/11427/5167 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5167
dc.identifier.vancouvercitationMazibuko TT. Feature extraction and normalization in SVM speaker verification using telephone speech. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2007 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5167en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleFeature extraction and normalization in SVM speaker verification using telephone speechen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_ebe_2007_mazibuko_t_t (1).pdf
Size:
4.9 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters