Probabilistic integration of joint density model and speaker model for voice conversion

Daisuke Saito, Shinji Watanabe, Atsushi Nakamura, Nobuaki Minematsu

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

This paper describes a novel approach to voice conversion using both a joint density model and a speaker model. In voice conversion studies, approaches based on Gaussian Mixture Model (GMM) with probabilistic densities of joint vectors of a source and a target speakers are widely used to estimate a transformation. However, for sufficient quality, they 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 over-training effects when the amount of training data is small. To compensate for these problems, we propose a novel approach to integrate the speaker GMM of the target with the joint density model using probabilistic formulation. The proposed method trains the joint density model with a few parallel utterances, and the speaker model with non-parallel data of the target, independently. It eases the burden on the source speaker. Experiments demonstrate the effectiveness of the proposed method, especially when the amount of the parallel corpus is small.

Original languageEnglish
Pages1728-1731
Number of pages4
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan
Duration: 2010 Sep 262010 Sep 30

Conference

Conference11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010
CountryJapan
CityMakuhari, Chiba
Period10/9/2610/9/30

Keywords

  • Joint density model
  • Probabilistic unification
  • Speaker model
  • Voice conversion

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing

Fingerprint Dive into the research topics of 'Probabilistic integration of joint density model and speaker model for voice conversion'. Together they form a unique fingerprint.

Cite this