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dc.contributor.advisor Manamela, M. J. D. Baloyi, Ntsako
dc.contributor.other Gasela, N. 2013-12-05T11:32:46Z 2013-12-05T11:32:46Z 2012
dc.description Thesis (M.Sc. (Computer Science) --University of Limpopo, 2013 en_US
dc.description.abstract This research study focuses on building a general-purpose working Xitsonga speech synthesis system that is as far as can be possible reasonably intelligible, natural sounding, and flexible. The system built has to be able to model some of the desirable speaker characteristics and speaking styles. This research project forms part of the broader national speech technology project that aims at developing spoken language systems for human-machine interaction using the eleven official languages of South Africa (SA). Speech synthesis is the reverse of automatic speech recognition (which receives speech as input and converts it to text) in that it receives text as input and produces synthesized speech as output. It is generally accepted that most people find listening to spoken utterances better that reading the equivalent of such utterances. The Xitsonga speech synthesis system has been developed using a hidden Markov model (HMM) speech synthesis method. The HMM-based speech synthesis (HTS) system synthesizes speech that is intelligible, and natural sounding. This method can synthesize speech on a footprint of only a few megabytes of training speech data. The HTS toolkit is applied as a patch to the HTK toolkit which is a hidden Markov model toolkit primarily designed for use in speech recognition to build and manipulate hidden Markov models. en_US
dc.format.extent x,84 leaves en_US
dc.language.iso en en_US
dc.publisher University of Limpopo (Turfloop Campus) en_US
dc.relation.requires PDF en_US
dc.subject Xitsonga speech synthesis system en_US
dc.subject Markov model en_US
dc.subject.ddc 006.454019 en_US
dc.subject.lcsh Xitsonga langauge en_US
dc.subject.lcsh Speech synthesis en_US
dc.subject.lcsh Speech processing systems en_US
dc.title A text-to-speech synthesis system for Xitsonga using hidden Markov models en_US
dc.type Thesis en_US

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