dc.contributor.advisor | Davel, M.H., Prof. | |
dc.contributor.author | Basson, Willem Diederick | |
dc.date.accessioned | 2014-08-13T07:09:23Z | |
dc.date.available | 2014-08-13T07:09:23Z | |
dc.date.issued | 2014 | |
dc.identifier.uri | http://hdl.handle.net/10394/11068 | |
dc.description | MSc (Computer Science) North-West University, Vaal Triangle Campus, 2014 | en_US |
dc.description.abstract | Grapheme-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.iso | en | en_US |
dc.publisher | North-West University | en_US |
dc.subject | Automatic speech recognition | en_US |
dc.subject | Phoneme-to-grapheme | en_US |
dc.subject | Transliteration | en_US |
dc.subject | Gra- pheme-based ASR | en_US |
dc.title | Improving Grapheme–based speech recognition through P2G transliteration | en |
dc.type | Thesis | en_US |
dc.description.thesistype | Masters | en_US |