Statistical voice conversion based on noisy channel model

Daisuke Saito*, Shinji Watanabe, Atsushi Nakamura, Nobuaki Minematsu


研究成果: Article査読

26 被引用数 (Scopus)


This paper describes a novel framework of voice conversion effectively using both a joint density model and a speaker model. In voice conversion studies, approaches based on the Gaussian mixture model (GMM) with probabilistic densities of joint vectors of a source and a target speakers are widely used to estimate a transform function between both the speakers. However, to achieve sufficient quality, these approaches require a parallel corpus which contains plenty of utterances with the same linguistic content spoken by both the speakers. In addition, the joint density GMM methods often suffer from overtraining effects when the amount of training data is small. To compensate for these problems, we propose a voice conversion framework, which integrates the speaker GMM of the target with the joint density model using a noisy channel model. The proposed method trains the joint density model with a few parallel utterances, and the speaker model with nonparallel data of the target, independently. It can ease the burden on the source speaker. Experiments demonstrate the effectiveness of the proposed method, especially when the amount of the parallel corpus is small.

ジャーナルIEEE Transactions on Audio, Speech and Language Processing
出版ステータスPublished - 2012

ASJC Scopus subject areas

  • 音響学および超音波学
  • 電子工学および電気工学


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