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Paper Abstract and Keywords
Presentation 2021-03-03 14:05
[Poster Presentation] A unified source-filter network for neural vocoder
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda (Nagoya Univ.) EA2020-69 SIP2020-100 SP2020-34
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a method to develop a neural vocoder using a single network based on the source-filter theory. A neural vocoder makes it possible to generate high-quality speech waveforms by applying a deep learning framework to direct speech waveform modeling. On the other hand, its controllability tends to be lower compared to that of a traditional vocoder due to the use of a totally data-driven framework. To alleviate this issue, there have been studied other neural vocoding frameworks consisting of a source excitation part and a resonance filtering part as in a traditional vocoding framework and applying a parametric model to one of these two parts. The use of a part of the traditional approximations is effective for improving controllability of neural vocoder. However, the resulting controllability is still insufficient, and this framework also causes an adverse effect on sound quality degradation compared to the totally data-driven framework. Towards the development of a better neural vocoder, we propose "a unified source-filter network" as a novel neural vocoding framework using a single network. The proposed network consists of cascaded two networks corresponding to the source excitation part and the resonance filtering part, making it possible to optimize all network parameters using a unified training criterion. Moreover, we try to optimize the source excitation network to generate reasonable source excitation signals by applying an additional constraint to its output. Our experimental results have demonstrated that the proposed method can improve $F_0$ controllability compared to the neural source-filter as one of the conventional neural vocoding methods.
Keyword (in Japanese) (See Japanese page) 
(in English) speech synthesis / source-filter model / neural vocoder / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 399, SP2020-34, pp. 57-62, March 2021.
Paper # SP2020-34 
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 EA2020-69 SIP2020-100 SP2020-34

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 unified source-filter network for neural vocoder 
Sub Title (in English)  
Keyword(1) speech synthesis  
Keyword(2) source-filter model  
Keyword(3) neural vocoder  
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1st Author's Name Reo Yoneyama  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Yi-Chiao Wu  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Tomoki Toda  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2021-03-03 14:05:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2020-69, SIP2020-100, SP2020-34 
Volume (vol) vol.120 
Number (no) no.397(EA), no.398(SIP), no.399(SP) 
Page pp.57-62 
#Pages 6 
Date of Issue 2021-02-24 (EA, SIP, SP) 

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