Paper Abstract and Keywords |
Presentation |
2018-03-20 09:00
[Poster Presentation]
Do prosodic manual annotations matter for Japanese speech synthesis systems with WaveNet vocoder? Hieu-Thi Luong, Xin Wang, Junichi Yamagishi (NII), Nobuyuki Nishizawa (KDDI Research) EA2017-140 SIP2017-149 SP2017-123 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We investigated the impact of noisy linguistics features on the performance of a Japanese neural net- work based speech synthesis system using a WaveNet vocoder. This investigation compared the ideal system using manually corrected linguistic features in training and test sets against a few other systems using corrupted linguistic features. Both subjective and objective results demonstrate that corrupted linguistic features, especially those in the test set, affected the system’s performance significantly in a statistical sense due to mismatched conditions between training and test sets. Interestingly, while an utterance-level Turing test shows that listeners had a difficult time to differentiate synthetic speech from natural speech, it further indicates that adding noise to the linguistic features in the training set partially can reduce the mismatched effect, regularize the model and help the system perform better when the test set linguistic features are noisy. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
speech synthesis / deep neural network / Japanese prosody / WaveNet / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 517, SP2017-123, pp. 215-220, March 2018. |
Paper # |
SP2017-123 |
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 |
EA2017-140 SIP2017-149 SP2017-123 |
Conference Information |
Committee |
SIP EA SP MI |
Conference Date |
2018-03-19 - 2018-03-20 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
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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 |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Do prosodic manual annotations matter for Japanese speech synthesis systems with WaveNet vocoder? |
Sub Title (in English) |
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Keyword(1) |
speech synthesis |
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deep neural network |
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Japanese prosody |
Keyword(4) |
WaveNet |
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1st Author's Name |
Hieu-Thi Luong |
1st Author's Affiliation |
National Institute of Informatics (NII) |
2nd Author's Name |
Xin Wang |
2nd Author's Affiliation |
National Institute of Informatics (NII) |
3rd Author's Name |
Junichi Yamagishi |
3rd Author's Affiliation |
National Institute of Informatics (NII) |
4th Author's Name |
Nobuyuki Nishizawa |
4th Author's Affiliation |
KDDI Research, Inc. (KDDI Research) |
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Speaker |
Author-1 |
Date Time |
2018-03-20 09:00:00 |
Presentation Time |
90 minutes |
Registration for |
SP |
Paper # |
EA2017-140, SIP2017-149, SP2017-123 |
Volume (vol) |
vol.117 |
Number (no) |
no.515(EA), no.516(SIP), no.517(SP) |
Page |
pp.215-220 |
#Pages |
6 |
Date of Issue |
2018-03-12 (EA, SIP, SP) |
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