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Voice Conversion Algorithm Based on Gaussian Mixture Model Applied to STRAIGHT

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dc.contributor.author Tomoki Toda en
dc.contributor.author Jinlin Lu en
dc.contributor.author Satoshi Nakamura en
dc.contributor.author Kiyohiro Shikano en
dc.date.accessioned 2012-08-30T02:23:26Z en
dc.date.available 2012-08-30T02:23:26Z en
dc.date.issued 2000-10 en
dc.identifier.isbn 9784998088615 en
dc.identifier.uri http://hdl.handle.net/10061/8282 en
dc.description WESTPRAC VII 2000: the 7th West Pacific Regional Acoustics Conference, October 3-5, 2000, Kumamoto, Japan. en
dc.description.abstract Voice conversion is a technique used to convert one speaker's voice into another speaker's voice. As a typical voice conversion algorithm, the codebook mapping method has been studied by Abe et al. The main shortcoming of this method is the fact that the acoustic space of a speaker is limited to a discrete representation. To represent the acoustic space continuously, the algorithm based on the Gaussian mixture model (GMM) has also been proposed by Stylianou et al. In this paper, we apply this GMM-based voice conversion algorithm to STRAIGHT proposed by Kawahara et al., which is recognized as a high quality vocoder. In order to evaluate this voice conversion algorithm, we performed subjective and objective experiments on speaker individuality and speech quality, comparing with the method based on the codebook mapping. As results, the performance of the GMM-based voice conversion algorithm is better than that of the codebook mapping method. Effects by the amount of training data for the voice conversion algorithms were also investigated, as well as the number of the Gaussian mixtures. These evaluation results clarify that the GMM-based voice conversion algorithm is successfully applied to STRAIGHT. en
dc.language.iso en en
dc.publisher 日本音響学会 ja
dc.subject voice conversion en
dc.subject codebook mapping en
dc.subject Gaussian mixture model en
dc.subject STRAIGHT en
dc.title Voice Conversion Algorithm Based on Gaussian Mixture Model Applied to STRAIGHT en
dc.type.nii Conference Paper en
dc.textversion Publisher en
dc.identifier.spage 169 en
dc.identifier.epage 172 en
dc.identifier.NAIST-ID 73296626 en
dc.identifier.NAIST-ID 73292716 en


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