Presentation 2014-12-16
Voice conversion based on deep neural network with multiple output sub-networks
Tetsuya HASHIMOTO, Yousuke KASHIWAGI, Daisuke SAITO, Keikichi HIROSE, Nobuaki MINEMATSU,
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Abstract(in English) This paper proposes a novel approach to construct a voice conversion (VC) system using deep neural networks (DNN) with the final goal to realize conversion to arbitrary speakers. Although DNN-based VC achieves certain performance improvement as compared to Gaussian Mixture Models (GMM) based VC, it has low flexibility in conversion processes. This is because functions of layers/nodes are difficult to be interpreted. In order to solve this problem, we introduce a new architecture that has one SI trunk and multiple SD branches, which are simultaneously trained by using parallel data from multiple speaker pairs. The objective evaluation demonstrates that the proposed architecture improves the performance of voice conversion.
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Keyword(in English) deep neural network / voice conversion / multiple-output / sub-network
Paper # SP2014-117
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Conference Information
Committee SP
Conference Date 2014/12/8(1days)
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Paper Information
Registration To Speech (SP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Voice conversion based on deep neural network with multiple output sub-networks
Sub Title (in English)
Keyword(1) deep neural network
Keyword(2) voice conversion
Keyword(3) multiple-output
Keyword(4) sub-network
1st Author's Name Tetsuya HASHIMOTO
1st Author's Affiliation Grad. School of Information Science and Technology, The Univ. of Tokyo()
2nd Author's Name Yousuke KASHIWAGI
2nd Author's Affiliation Grad. School of Engineering, The Univ. of Tokyo
3rd Author's Name Daisuke SAITO
3rd Author's Affiliation Grad. School of Information Science and Technology, The Univ. of Tokyo
4th Author's Name Keikichi HIROSE
4th Author's Affiliation Grad. School of Information Science and Technology, The Univ. of Tokyo
5th Author's Name Nobuaki MINEMATSU
5th Author's Affiliation Grad. School of Engineering, The Univ. of Tokyo
Date 2014-12-16
Paper # SP2014-117
Volume (vol) vol.114
Number (no) 365
Page pp.pp.-
#Pages 6
Date of Issue