| Presentation | 2018-03-19 On the Use of Deep Gaussian Processes for GPR-based Speech Synthesis Tomoki Koriyama, Takao Kobayashi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper proposes a speech synthesis frameworkbased on deep Gaussian processes (DGPs). DGP is a Bayesian deep learning modelthat is composed of stacked Gaussian process regression. In our preliminary experiments, DGP-based system yieldedmore natural-sounding synthetic speech than DNN-based one. However, the performance evaluation of DGP had not been done in detail. In this paper, we perform speech synthesis under various experimental conditionswith chainging kernel function and the number of layers, and examine the relationships between acoustic feature distortionsand model architectures. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | deep Gaussian process / stochastic variational inference / statistical parametric speech synthesis |
| Paper # | EA2017-106,SIP2017-115,SP2017-89 |
| Date of Issue | 2018-03-12 (EA, SIP, SP) |
| Conference Information | |
| Committee | SIP / EA / SP / MI |
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| Conference Date | 2018/3/19(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics [SIP, EA, SP]/ Medical Image Engineering, Analysis, Recognition, etc. [MI] |
| Chair | Masahiro Okuda(Univ. of Kitakyushu) / Suehiro Shimauchi(NTT) / Yoichi Yamashita(Ritsumeikan Univ.) / Kensaku Mori(Nagoya Univ.) |
| Vice Chair | Shogo Muramatsu(Niigata Univ.) / Naoyuki Aikawa(TUS) / Mitsunori Mizumachi(Kyutech) / Hiroki Mori(Utsunomiya Univ.) / Yoshiki Kawata(Tokushima Univ.) / Yuichi Kimura(Kinki Univ.) |
| Secretary | Shogo Muramatsu(Chiba Inst. of Tech.) / Naoyuki Aikawa(Takushoku Univ.) / Mitsunori Mizumachi(Akita Pref. Univ.) / Hiroki Mori(Shizuoka Inst. of Science and Tech.) / Yoshiki Kawata(Shizuoka Univ.) / Yuichi Kimura(Meijo Univ.) |
| Assistant | Masayoshi Nakamoto(Hiroshima Univ.ひろ) / TREVINO Jorge(Tohoku Univ.) / Nobutaka Ito(NTT) / Kei Hashimoto(Nagoya Inst. of Tech.) / Satoshi Kobashikawa(NTT) / Ryo Haraguchi(Univ. of Hyogo) / Yasushi Hirano(Yamaguchi Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Signal Processing / Technical Committee on Engineering Acoustics / Technical Committee on Speech / Technical Committee on Medical Imaging |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | On the Use of Deep Gaussian Processes for GPR-based Speech Synthesis |
| Sub Title (in English) | |
| Keyword(1) | deep Gaussian process |
| Keyword(2) | stochastic variational inference |
| Keyword(3) | statistical parametric speech synthesis |
| 1st Author's Name | Tomoki Koriyama |
| 1st Author's Affiliation | Tokyo Institute of Technology(Tokyo Inst. of Tech.) |
| 2nd Author's Name | Takao Kobayashi |
| 2nd Author's Affiliation | Tokyo Institute of Technology(Tokyo Inst. of Tech.) |
| Date | 2018-03-19 |
| Paper # | EA2017-106,SIP2017-115,SP2017-89 |
| Volume (vol) | vol.117 |
| Number (no) | EA-515,SIP-516,SP-517 |
| Page | pp.pp.27-32(EA), pp.27-32(SIP), pp.27-32(SP), |
| #Pages | 6 |
| Date of Issue | 2018-03-12 (EA, SIP, SP) |