| Presentation | 2014-06-19 Individuality-preserving Voice Conversion for Articulation Disorders Using Sparse Dictionary Learning Ryo AIHARA, Tetsuya TAKIGUCHI, Yasuo ARIKI, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We present in this paper a voice conversion (VC) method for a person with an articulation disorder resulting from athetoid cerebral palsy. The movement of such speakers is limited by their athetoid symptoms, and their consonants are often unstable or unclear, which makes it difficult for them to communicate. In our previous method, exemplar-based spectral conversion using Non-negative Matrix Factorization (NMF) was applied to a voice with an articulation disorder. To preserve the speaker's individuality, we used a combined dictionary that is constructed from the source speaker's vowels and target speaker's consonants. However, in this exemplar-based approach, source speaker's activity matrix which is estimated from input spectra and source speaker's exemplars are used as target speaker's. In this paper, we propose a sparse dictionary learning method for exemplar-based VC and estimate a mapping matrix between source speaker's activity and target speaker's activity. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method and a conventional NMF-based method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Voice Conversion / Articulation Disorders / Asistive Technology / Non-negative Matrix Factorization |
| Paper # | SP2014-53,WIT2014-8 |
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| Conference Information | |
| Committee | WIT |
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| Conference Date | 2014/6/12(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Well-being Information Technology(WIT) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Individuality-preserving Voice Conversion for Articulation Disorders Using Sparse Dictionary Learning |
| Sub Title (in English) | |
| Keyword(1) | Voice Conversion |
| Keyword(2) | Articulation Disorders |
| Keyword(3) | Asistive Technology |
| Keyword(4) | Non-negative Matrix Factorization |
| 1st Author's Name | Ryo AIHARA |
| 1st Author's Affiliation | Graduate School of System Informatics, Kobe University() |
| 2nd Author's Name | Tetsuya TAKIGUCHI |
| 2nd Author's Affiliation | Organization of Advanced Science and Technology, Kobe University |
| 3rd Author's Name | Yasuo ARIKI |
| 3rd Author's Affiliation | Organization of Advanced Science and Technology, Kobe University |
| Date | 2014-06-19 |
| Paper # | SP2014-53,WIT2014-8 |
| Volume (vol) | vol.114 |
| Number (no) | 92 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |