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Paper Abstract and Keywords
Presentation 2021-03-04 17:10
A Vocoder-free Any-to-Many Voice Conversion using Pre-trained vq-wav2vec
Takeshi Koshizuka, Hidefumi Ohmura, Kouichi Katsurada (TUS) EA2020-89 SIP2020-120 SP2020-54
Abstract (in Japanese) (See Japanese page) 
(in English) Voice conversion (VC) is a technique that converts speaker-dependent non-linguistic information to another speaker's one while retaining the linguistic information of input speeches. A typical VC system is composed of two modules: an encoder module which removes speaker individuality from the speech, and a decoder module which incorporates another speaker's individuality to the synthesized speech. In this paper, we propose a vocoder-free any-to-many voice conversion model using the pre-trained vq-wav2vec as an encoder module. Our model makes it possible to convert speech using only a small amount of training data by pre-training the RNN_MS like decoder module in addition to pre-training the encoder module. The difference from the previous approach which also pre-trains both the encoder and the decoder modules is that our target is any-to-many voice conversion and the decoder module is pre-trained with the voice conversion task. The experimental results show that we could obtain good conversion performance. We have also confirmed the system can add new target speakers without deteriorating the performance of conversion for the pre-trained target speakers.
Keyword (in Japanese) (See Japanese page) 
(in English) Any-to-Many Voice Conversion / Encoder-Decoder model / Pre-training / vq-wav2vec / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 399, SP2020-54, pp. 176-181, March 2021.
Paper # SP2020-54 
Date of Issue 2021-02-24 (EA, SIP, 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 EA2020-89 SIP2020-120 SP2020-54

Conference Information
Committee EA US SP SIP IPSJ-SLP  
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, Ultrasonics, and Related Topics 
Paper Information
Registration To SP 
Conference Code 2021-03-EA-US-SP-SIP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Vocoder-free Any-to-Many Voice Conversion using Pre-trained vq-wav2vec 
Sub Title (in English)  
Keyword(1) Any-to-Many Voice Conversion  
Keyword(2) Encoder-Decoder model  
Keyword(3) Pre-training  
Keyword(4) vq-wav2vec  
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1st Author's Name Takeshi Koshizuka  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Hidefumi Ohmura  
2nd Author's Affiliation Tokyo University of Science (TUS)
3rd Author's Name Kouichi Katsurada  
3rd Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2021-03-04 17:10:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # EA2020-89, SIP2020-120, SP2020-54 
Volume (vol) vol.120 
Number (no) no.397(EA), no.398(SIP), no.399(SP) 
Page pp.176-181 
#Pages 6 
Date of Issue 2021-02-24 (EA, SIP, SP) 

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