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dc.contributor.authorVan Niekerk, Daniel Rudolph
dc.date.accessioned2011-02-18T06:43:38Z
dc.date.available2011-02-18T06:43:38Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10394/3978
dc.descriptionThesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.
dc.description.abstractThe rapid development of corpus-based speech systems such as concatenative synthesis systems for under-resourced languages requires an efficient, consistent and accurate solution with regard to phonetic speech segmentation. Manual development of phonetically annotated corpora is a time consuming and expensive process which suffers from challenges regarding consistency and reproducibility, while automation of this process has only been satisfactorily demonstrated on large corpora of a select few languages by employing techniques requiring extensive and specialised resources. In this work we considered the problem of phonetic segmentation in the context of developing small prototypical speech synthesis corpora for new under-resourced languages. This was done through an empirical evaluation of existing segmentation techniques on typical speech corpora in three South African languages. In this process, the performance of these techniques were characterised under different data conditions and the efficient application of these techniques were investigated in order to improve the accuracy of resulting phonetic alignments. We found that the application of baseline speaker-specific Hidden Markov Models results in relatively robust and accurate alignments even under extremely limited data conditions and demonstrated how such models can be developed and applied efficiently in this context. The result is segmentation of sufficient quality for synthesis applications, with the quality of alignments comparable to manual segmentation efforts in this context. Finally, possibilities for further automated refinement of phonetic alignments were investigated and an efficient corpus development strategy was proposed with suggestions for further work in this direction.
dc.publisherNorth-West University
dc.subjectPhonetic speech segmentationen
dc.subjectPhonetic alignmenten
dc.subjectSpeech synthesisen
dc.subjectText-to-speechen
dc.subjectSpeech corpus developmenten
dc.subjectResource scarce languagesen
dc.subjectHidden Markov modelsen
dc.subjectDynamic time warpingen
dc.titleAutomatic speech segmentation with limited dataen
dc.typeThesisen
dc.description.thesistypeMasters


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