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Automatic classification of spoken South African English variants using a transcription-less speech recognition approach

dc.contributor.advisorDu Preez, J. A.
dc.contributor.authorDu Toit, A. (Andre)
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.en_ZA
dc.date.accessioned2012-08-27T11:33:08Z
dc.date.available2012-08-27T11:33:08Z
dc.date.issued2004-03
dc.identifier.urihttp://hdl.handle.net/10019.1/49866
dc.descriptionThesis (MEng)--University of Stellenbosch, 2004.en_ZA
dc.description.abstractENGLISH ABSTRACT: We present the development of a pattern recognition system which is capable of classifying different Spoken Variants (SVs) of South African English (SAE) using a transcriptionless speech recognition approach. Spoken Variants (SVs) allow us to unify the linguistic concepts of accent and dialect from a pattern recognition viewpoint. The need for the SAE SV classification system arose from the multi-linguality requirement for South African speech recognition applications and the costs involved in developing such applications.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Ons beskryf die ontwikkeling van 'n patroon herkenning stelsel wat in staat is om verskillende Gesproke Variante (GVe) van Suid Afrikaanse Engels (SAE) te klassifiseer met behulp van 'n transkripsielose spraak herkenning metode. Gesproke Variante (GVe) stel ons in staat om die taalkundige begrippe van aksent en dialek te verenig vanuit 'n patroon her kenning oogpunt. Die behoefte aan 'n SAE GV klassifikasie stelsel het ontstaan uit die meertaligheid vereiste vir Suid Afrikaanse spraak herkenning stelsels en die koste verbonde aan die ontwikkeling van sodanige stelsels.af_ZA
dc.format.extent156 leaves : ill.
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.subjectAutomatic speech recognitionen_ZA
dc.subjectPattern recognition systemsen_ZA
dc.subjectSpeech processing systemsen_ZA
dc.subjectDissertations -- Electrical and electronic engineeringen_ZA
dc.subjectTheses -- Electrical and electronic engineeringen_ZA
dc.titleAutomatic classification of spoken South African English variants using a transcription-less speech recognition approachen_ZA
dc.typeThesisen_ZA
dc.rights.holderStellenbosch Universityen_ZA


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