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Paper Abstract and Keywords
Presentation 2018-01-20 14:55
[Poster Presentation] A study on the articulatory-to-speech conversion by using deep learning
Fumiaki Taguchi, Tokihiko Kaburagi (Kyushu Univ.) SP2017-70
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we examined a method to convert the movement pattern of articulatory organs observed by a magnetic sensor (EMA) into feature parameters of speech. In conventional studies, articulation parameters representing movement pattern of articulatory organs were usually converted to feature parameters representing the spectral envelope of the speech, because articulation parameters are directly related to the acoustic characteristics of the vocal tract. However, articulatory parameters and the acoustic characteristics of the vocal tract are responsible for the phonological properties of speech and phonemic information is related to glottal sound source information such as the pitch pattern and the voiced-unvoiced distinction. These considerations suggest that there exists a certain kind of relationship between articulatory parameters and the glottal sound source information. In this study, we relied on this relationship and estimated not only the spectral envelope but also features related to the glottal sound source, thereby synthesizing speech directly from the movement orbit of articulatory organs. We also objectively evaluated the estimation accuracy of speech parameters.
Keyword (in Japanese) (See Japanese page) 
(in English) articulatory movement / vocal tract spectrum / Deep Learning / articulatory-to-acoustic mapping / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 393, SP2017-70, pp. 27-30, Jan. 2018.
Paper # SP2017-70 
Date of Issue 2018-01-13 (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. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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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)  
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 the articulatory-to-speech conversion by using deep learning 
Sub Title (in English)  
Keyword(1) articulatory movement  
Keyword(2) vocal tract spectrum  
Keyword(3) Deep Learning  
Keyword(4) articulatory-to-acoustic mapping  
1st Author's Name Fumiaki Taguchi  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Tokihiko Kaburagi  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2018-01-20 14:55:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # SP2017-70 
Volume (vol) vol.117 
Number (no) no.393 
Page pp.27-30 
Date of Issue 2018-01-13 (SP) 

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