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An improved voice conversion method using segmental GMMs and automatic GMM selection

Authors: Hung-Yan Gu; Sung-Fung Tsai;

An improved voice conversion method using segmental GMMs and automatic GMM selection

Abstract

In this paper, the idea of segmental GMMs is proposed for voice conversion. Also, to apply this idea to on-line voice conversion, we have developed an automatic GMM selection algorithm based on dynamic programming. In addition, to map a vector of DCC (discrete cepstrum coefficients) with only one Gaussian mixture, we have designed a mixture selection algorithm. For evaluating the performance of the idea, segmental GMMs, three voice conversion system are constructed and used to conduct listening tests. The results of the listening tests show that segmental GMMs proposed here can indeed help to improve the performances in both timbre similarity and voice quality.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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