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Paper Abstract and Keywords
Presentation 2021-06-19 15:00
Neural speech synthesis using local phrase dependency structure information
Nobuyoshi Kaiki, Sakriani Sakti, Satoshi Nakamura (NIST) SP2021-23
Abstract (in Japanese) (See Japanese page) 
(in English) In order to synthesize Japanese speech with natural prosody, we introduce an end-to-end TTS with new prosodic symbol representing phrase components based on local phrase dependency structures to end-to-end text-to-speech synthesis (TTS). In this paper, we propose two TTS models: 1) a model with prosodic symbols that represent the depth at phrase boundaries, and 2) a model with prosodic symbols that reflects a folded model of phrase and accent components based on a prosodic generation control mechanism. In synthesized speech at left-branching boundary using these two models, 1) pause indicating the phrase boundary is generated. 2) the re-rebuilding phrase component of F0 may occur. To verify the effect of these two proposed models on a conventional model using prosodic symbols using only accent components, we conducted a subjective evaluation on the AB test. As a result, it was confirmed that by using local phrase boundary information of sentences and prosodic generation model in Japanese end-to-end text-to-speech synthesis, synthetic speech with more natural prosody that reflects the intention of the utterance could be generated.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural end-to-end text-to-speech speech synthesis / Local phrase dependency structure / Prosodic symbol / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 66, SP2021-23, pp. 107-112, June 2021.
Paper # SP2021-23 
Date of Issue 2021-06-11 (SP) 
ISSN 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 SP2021-23

Conference Information
Committee SP IPSJ-SLP IPSJ-MUS  
Conference Date 2021-06-18 - 2021-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) OTOGAKU Symposium 2021 
Paper Information
Registration To SP 
Conference Code 2021-06-SP-SLP-MUS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neural speech synthesis using local phrase dependency structure information 
Sub Title (in English)  
Keyword(1) Neural end-to-end text-to-speech speech synthesis  
Keyword(2) Local phrase dependency structure  
Keyword(3) Prosodic symbol  
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1st Author's Name Nobuyoshi Kaiki  
1st Author's Affiliation Nara Institute of science and Technology (NIST)
2nd Author's Name Sakriani Sakti  
2nd Author's Affiliation Nara Institute of science and Technology (NIST)
3rd Author's Name Satoshi Nakamura  
3rd Author's Affiliation Nara Institute of science and Technology (NIST)
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Speaker Author-1 
Date Time 2021-06-19 15:00:00 
Presentation Time 120 minutes 
Registration for SP 
Paper # SP2021-23 
Volume (vol) vol.121 
Number (no) no.66 
Page pp.107-112 
#Pages 6 
Date of Issue 2021-06-11 (SP) 

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