Paper Abstract and Keywords |
Presentation |
2018-06-28 15:10
Multimodal voice conversion using deep bottleneck features and deep canonical correlation analysis Satoshi Tamura, Kento Horio, Hajime Endo, Satoru Hayamizu (Gifu Univ.), Tomoki Toda (Nagoya Univ.) PRMU2018-24 SP2018-4 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we aim at improving the speech quality in voice conversion and propose a novel multi-modal voice conversion approach using speech waveforms and lip images.
We employ deep bottleneck features to improve visual features in audio-visual voice conversion.
In addition, we also apply deep canonical correlation analysis to obtain much better audio and visual representations, as well as to build a new cross-modal framework.
We conducted subjective and objective evaluations in noisy environments to clarify usefulness of our proposed method, comparing to audio-only, visual-only and conventional audio-visual voice conversion schemes.
We then found our method can significantly improve the quality even in heavily noisy conditions. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Voice conversion / multi-modal / audio-visual / cross-modal / deep learning / bottleneck feature / canonical component analysis / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 112, SP2018-4, pp. 13-18, June 2018. |
Paper # |
SP2018-4 |
Date of Issue |
2018-06-21 (PRMU, 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) |
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PRMU2018-24 SP2018-4 |
Conference Information |
Committee |
PRMU SP |
Conference Date |
2018-06-28 - 2018-06-29 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
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Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SP |
Conference Code |
2018-06-PRMU-SP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Multimodal voice conversion using deep bottleneck features and deep canonical correlation analysis |
Sub Title (in English) |
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Keyword(1) |
Voice conversion |
Keyword(2) |
multi-modal |
Keyword(3) |
audio-visual |
Keyword(4) |
cross-modal |
Keyword(5) |
deep learning |
Keyword(6) |
bottleneck feature |
Keyword(7) |
canonical component analysis |
Keyword(8) |
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1st Author's Name |
Satoshi Tamura |
1st Author's Affiliation |
Gifu University (Gifu Univ.) |
2nd Author's Name |
Kento Horio |
2nd Author's Affiliation |
Gifu University (Gifu Univ.) |
3rd Author's Name |
Hajime Endo |
3rd Author's Affiliation |
Gifu University (Gifu Univ.) |
4th Author's Name |
Satoru Hayamizu |
4th Author's Affiliation |
Gifu University (Gifu Univ.) |
5th Author's Name |
Tomoki Toda |
5th Author's Affiliation |
Nagoya University (Nagoya Univ.) |
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Speaker |
Author-1 |
Date Time |
2018-06-28 15:10:00 |
Presentation Time |
30 minutes |
Registration for |
SP |
Paper # |
PRMU2018-24, SP2018-4 |
Volume (vol) |
vol.118 |
Number (no) |
no.111(PRMU), no.112(SP) |
Page |
pp.13-18 |
#Pages |
6 |
Date of Issue |
2018-06-21 (PRMU, SP) |
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