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

Du Toit, A. (Andre) (2004-03)

Thesis (MEng)--University of Stellenbosch, 2004.

Thesis

ENGLISH 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.

AFRIKAANSE 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.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/49866
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