Paper Abstract and Keywords |
Presentation |
2018-01-20 13:25
A study on statistical speech synthesis based on GP-DNN hybrid model Tomoki Koriyama, Takao Kobayashi (Tokyo Tech) SP2017-67 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a novel approach to Gaussian process regression (GPR)-based speech synthesis
in this paper.
Since the conventional GPR-based speech synthesis was based on data partition with a decision tree,
a decision tree was bottleneck of the performance of synthetic speech.
In contrast, we propose a hybrid model of Gaussian process and deep neural network (DNN).
In the hybrid model, DNN extracts context-derived features
and the output of DNN is used as an input of Gaussian process.
The parameters of DNN and GP are optimized using a minibatch-based
stochastic gradient descent method.
From the subjective evaluation results,
it can be seen that the proposed technique outperforms not only the conventional
GPR-based speech synthesis with decision trees
but also DNN-based speech synthesis. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Gaussian process regression / stochastic variational inference / neural network / statistical parametric speech synthesis / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 393, SP2017-67, pp. 5-10, Jan. 2018. |
Paper # |
SP2017-67 |
Date of Issue |
2018-01-13 (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) |
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SP2017-67 |
Conference Information |
Committee |
SP ASJ-H |
Conference Date |
2018-01-20 - 2018-01-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
The University of Tokyo |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
SP |
Conference Code |
2018-01-SP-H |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A study on statistical speech synthesis based on GP-DNN hybrid model |
Sub Title (in English) |
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Keyword(1) |
Gaussian process regression |
Keyword(2) |
stochastic variational inference |
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neural network |
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statistical parametric speech synthesis |
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1st Author's Name |
Tomoki Koriyama |
1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
2nd Author's Name |
Takao Kobayashi |
2nd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
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Speaker |
Author-1 |
Date Time |
2018-01-20 13:25:00 |
Presentation Time |
25 minutes |
Registration for |
SP |
Paper # |
SP2017-67 |
Volume (vol) |
vol.117 |
Number (no) |
no.393 |
Page |
pp.5-10 |
#Pages |
6 |
Date of Issue |
2018-01-13 (SP) |
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