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| Presentation | 2018-03-19 10:50 On the Use of Deep Gaussian Processes for GPR-based Speech Synthesis Tomoki Koriyama, Takao Kobayashi (Tokyo Inst. of Tech.)  EA2017-106 SIP2017-115 SP2017-89 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper proposes a speech synthesis framework based on deep Gaussian processes (DGPs). DGP is a Bayesian deep learning model that is composed of stacked Gaussian process regression. In our preliminary experiments, DGP-based system yielded more 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 conditions with chainging kernel function and the number of layers, and examine the relationships between acoustic feature distortions and model architectures. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | deep Gaussian process / stochastic variational inference / statistical parametric speech synthesis / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 517, SP2017-89, pp. 27-32, March 2018. | |
| Paper # | SP2017-89 | |
| Date of Issue | 2018-03-12 (EA, SIP, SP) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | EA2017-106 SIP2017-115 SP2017-89 |
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| Conference Information | |
| Committee | SIP EA SP MI |
| Conference Date | 2018-03-19 - 2018-03-20 |
| 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] |
| Paper Information | |
| Registration To | SP |
| Conference Code | 2018-03-SIP-EA-SP-MI |
| Language | Japanese |
| 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 |
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| 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.) |
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| Speaker | Author-1 |
| Date Time | 2018-03-19 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | SP |
| Paper # | EA2017-106, SIP2017-115, SP2017-89 |
| Volume (vol) | vol.117 |
| Number (no) | no.515(EA), no.516(SIP), no.517(SP) |
| Page | pp.27-32 |
| #Pages | 6 |
| Date of Issue | 2018-03-12 (EA, SIP, SP) |
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