| Presentation | 2012-11-08 Effects of speaker adaptive training on arbitrary speaker conversion based on tensor representation Daisuke SAITO, Nobuaki MINEMATSU, Keikichi HIROSE, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, speaker adaptive training techniques are introduced to tensor-based arbitrary speaker conversion. In voice conversion studies, realization of conversion from/to an arbitrary speaker's voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC), which is based on an eigenvoice Gaussian mixture model (EV-GMM), was proposed. Although the EVC can effectively construct the conversion model for arbitrary target speakers using only a few utterances, increase of the utterances used to construct the conversion model does not always improve the conversion performance. This is because the EV-GMM method has an inherent problem in representation of GMM supervectors. We previously proposed tensor-based speaker space as a solution for this problem, and realized more flexible control of speaker characteristics. In this paper, to aim larger improvement of the performance of VC, speaker adaptive training and tensor-based speaker representation are integrated. The proposed method can construct the flexible and precise conversion model, and experimental results of one-to-many voice conversion demonstrate the effectiveness of the proposed approach. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Voice conversion / Gaussian mixture model / Tucker decomposition / speaker adaptive training |
| Paper # | SP2012-72 |
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| Committee | SP |
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| Conference Date | 2012/11/1(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Speech (SP) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Effects of speaker adaptive training on arbitrary speaker conversion based on tensor representation |
| Sub Title (in English) | |
| Keyword(1) | Voice conversion |
| Keyword(2) | Gaussian mixture model |
| Keyword(3) | Tucker decomposition |
| Keyword(4) | speaker adaptive training |
| 1st Author's Name | Daisuke SAITO |
| 1st Author's Affiliation | Graduate School of Information Science and Technology, The University of Tokyo() |
| 2nd Author's Name | Nobuaki MINEMATSU |
| 2nd Author's Affiliation | Graduate School of Engineering, The University of Tokyo |
| 3rd Author's Name | Keikichi HIROSE |
| 3rd Author's Affiliation | Graduate School of Information Science and Technology, The University of Tokyo |
| Date | 2012-11-08 |
| Paper # | SP2012-72 |
| Volume (vol) | vol.112 |
| Number (no) | 281 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |