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Paper Abstract and Keywords
Presentation 2019-12-06 13:55
[Poster Presentation] Effectiveness of sequence-to-sequence acoustic modeling by using automatic generated labels
Kiyoshi Kurihara, Nobumasa Seiyama, Tadashi Kumano (NHK) SP2019-37
Abstract (in Japanese) (See Japanese page) 
(in English) We have proposed a method that uses yomigana (Japanese character readings) and prosodic symbols as input for sequence-to-sequence acoustic modeling in end-to-end speech synthesis. Sequence-to-sequence acoustic modeling can associate language features with acoustic features automatically, making it unnecessary to perform expensive phoneme segmentation work. In this paper, we propose a method for generating synthesized speech by automatically generating yomigana and prosodic symbols from sentences containing a mixture of kanji and kana characters. This method uses the front-end of Open JTalk (an open-source Japanese speech synthesis system) to obtain full-context labels from a kanji-kana mixed sentence, and then automatically converts these full-context labels into yomigana and prosodic symbols. By automating the learning units from the front-end to the sequence-to-sequence acoustic modeling , we can achieve high-quality speech synthesis learning from kanji-kana mixed sentences and speech files without incurring any additional cost. In subjective evaluation tests, we were able to confirm the efficacy of the proposed method. With our proposed method, it is possible to achieve speech synthesis of consistent quality based on yomigana and prosodic symbols obtained by automatically converting kanji-kana mixed text. This results in a considerable cost merit when implementing speech synthesis from newly recorded speech.
Keyword (in Japanese) (See Japanese page) 
(in English) Statistical parametric speech synthesis / End-to-end speech synthesis / Prosodic symbols / Sequence-to-sequence model / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 321, SP2019-37, pp. 49-54, Dec. 2019.
Paper # SP2019-37 
Date of Issue 2019-11-29 (SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF SP2019-37

Conference Information
Conference Date 2019-12-04 - 2019-12-06 
Place (in Japanese) (See Japanese page) 
Place (in English) NHK Science & Technology Research Labs. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 6th Natural Language Processing Symposium & The 21th Spoken Language Symposium 
Paper Information
Registration To SP 
Conference Code 2019-12-NLC-NL-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Effectiveness of sequence-to-sequence acoustic modeling by using automatic generated labels 
Sub Title (in English)  
Keyword(1) Statistical parametric speech synthesis  
Keyword(2) End-to-end speech synthesis  
Keyword(3) Prosodic symbols  
Keyword(4) Sequence-to-sequence model  
1st Author's Name Kiyoshi Kurihara  
1st Author's Affiliation NHK STRL (NHK)
2nd Author's Name Nobumasa Seiyama  
2nd Author's Affiliation NHK STRL (NHK)
3rd Author's Name Tadashi Kumano  
3rd Author's Affiliation NHK STRL (NHK)
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Date Time 2019-12-06 13:55:00 
Presentation Time 90 
Registration for SP 
Paper # IEICE-SP2019-37 
Volume (vol) IEICE-119 
Number (no) no.321 
Page pp.49-54 
#Pages IEICE-6 
Date of Issue IEICE-SP-2019-11-29 

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