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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)  
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)  
Keyword(1) speech synthesis  
Keyword(2) deep neural network  
Keyword(3) 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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