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dc.contributor.advisorDavel, M.H., Prof.
dc.contributor.authorBasson, Willem Diederick
dc.date.accessioned2014-08-13T07:09:23Z
dc.date.available2014-08-13T07:09:23Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10394/11068
dc.descriptionMSc (Computer Science) North-West University, Vaal Triangle Campus, 2014en_US
dc.description.abstractGrapheme-based speech recognition systems are faster to develop, but typically do not reach the same level of performance as phoneme-based systems. Using Afrikaans speech recognition as a case study, we first analyse the reasons for the discrepancy in performance, before introducing a technique for improving the performance of standard grapheme-based systems. It is found that by handling a relatively small number of irregular words through phoneme-to-grapheme (P2G) transliteration – transforming the original orthography of irregular words to an ‘idealised’ orthography – grapheme-based accuracy can be improved. An analysis of speech recognition accuracy based on word categories shows that P2G transliteration succeeds in improving certain word categories in which grapheme-based systems typically perform poorly, and that the problematic categories can be identified prior to system development. An evaluation is offered of when category-based P2G transliteration is beneficial and methods to implement the technique in practice are discussed. Comparative results are obtained for a second language (Vietnamese) in order to determine whether the technique can be generalised.en_US
dc.language.isoenen_US
dc.publisherNorth-West Universityen_US
dc.subjectAutomatic speech recognitionen_US
dc.subjectPhoneme-to-graphemeen_US
dc.subjectTransliterationen_US
dc.subjectGra- pheme-based ASRen_US
dc.titleImproving Grapheme–based speech recognition through P2G transliterationen
dc.typeThesisen_US
dc.description.thesistypeMastersen_US


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