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An Improved One-to-Many Eigenvoice Conversion System

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dc.contributor.author Yamato Ohtani en
dc.contributor.author Tomoki Toda en
dc.contributor.author Hiroshi Saruwatari en
dc.contributor.author Kiyohiro Shikano en
dc.date.accessioned 2012-08-22T07:58:55Z en
dc.date.available 2012-08-22T07:58:55Z en
dc.date.issued 2008-09 en
dc.identifier.uri http://hdl.handle.net/10061/8156 en
dc.description INTERSPEECH2008: 9th Annual Conference of the International Speech Communication Association, September 22-26, 2008, Brisbane, Australia. en
dc.description.abstract We have previously developed a one-to-many eigenvoice conversion (EVC) system enabling the conversion from a specific source speaker's voice into an arbitrary target speaker's voice. In this system, eigenvoice Gaussian mixture model (EV-GMM) is trained in advance with multiple parallel data sets composed of utterance pairs of the source and many pre-stored target speakers. The EV-GMM is effectively adapted to an arbitrary target speaker using a small amount of adaptation data. Although this system achieves the very flexible training of the conversion model, the quality of the converted speech is still not high enough. In order to alleviate this problem, we simultaneously apply the following promising techniques to the one-to-many EVC system: 1) STRAIGHT mixed excitation, 2) the conversion algorithm considering global variance, and 3) speaker adaptive training of the EV-GMM. Experimental results demonstrate that the proposed system causes remarkable improvements in the performance of EVC. en
dc.language.iso en en
dc.rights Copyright 2008 ISCA en
dc.subject speech synthesis en
dc.subject eigenvoice conversion en
dc.subject speaker adaptive training en
dc.subject mixed excitation en
dc.subject global variance en
dc.title An Improved One-to-Many Eigenvoice Conversion System en
dc.type.nii Conference Paper en
dc.textversion Publisher en
dc.identifier.spage 1080 en
dc.identifier.epage 1083 en
dc.identifier.NAIST-ID 73292716 en

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