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 |
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) |
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SP2017-70 |
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) |
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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) |
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articulatory movement |
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vocal tract spectrum |
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Deep Learning |
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articulatory-to-acoustic mapping |
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1st Author's Name |
Fumiaki Taguchi |
1st Author's Affiliation |
Kyushu University (Kyushu Univ.) |
2nd Author's Name |
Tokihiko Kaburagi |
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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 |
#Pages |
4 |
Date of Issue |
2018-01-13 (SP) |
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