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Low-Delay Voice Conversion Based on Maximum Likelihood Estimation of Spectral Parameter Trajectory

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dc.contributor.author Takashi Muramatsu en
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/10061/8155 en
dc.description INTERSPEECH2008: 9th Annual Conference of the International Speech Communication Association, September 22-26, 2008, Brisbane, Australia. en
dc.description.abstract As typical voice conversion methods, two spectral conversion processes have been proposed: 1) the frame-based conversion that converts spectral parameters frame by frame and 2) the trajectory-based conversion that converts all spectral parameters over an utterance simultaneously. The former process is capable of real-time conversion but it sometimes causes inappropriate spectral movements. On the other hand, the latter process provides the converted spectral parameters exhibiting proper dynamic characteristics but a batch process is inevitable. To achieve the real-time conversion process considering spectral dynamic characteristics, we propose a time-recursive conversion algorithm based on maximum likelihood estimation of spectral parameter trajectory. Experimental results show that the proposed method achieves the low-delay conversion process, e.g., only one frame delay, while keeping the conversion performance comparably high to that of the conventional trajectory-based conversion. en
dc.language.iso en en
dc.rights Copyright 2008 ISCA en
dc.subject speech synthesis en
dc.subject voice conversion en
dc.subject Gaussian mixture model en
dc.subject maximum likelihood estimation en
dc.subject time-recursive algorithm en
dc.title Low-Delay Voice Conversion Based on Maximum Likelihood Estimation of Spectral Parameter Trajectory en
dc.type.nii Conference Paper en
dc.textversion Publisher en
dc.identifier.spage 1076 en
dc.identifier.epage 1079 en
dc.identifier.NAIST-ID 73292716 en

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