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Paper Abstract and Keywords
Presentation 2022-06-18 15:00
[Poster Presentation] Worker Filtering Criteria for Subjective Evaluation of Synthesized Voice Sound Quality Using Crowdsourcing
Moe Yaegashi (Waseda Univ.), Susumu Saito, Teppei Nakano (Waseda Univ./ifLab.), Tetsuji Ogawa (Waseda Univ.) SP2022-24
Abstract (in Japanese) (See Japanese page) 
(in English) We investigate the effect of filtering criteria of crowdworkers on the subjective evaluation results of synthesized voice using crowdsourcing. Currently, crowdsourcing has been used for subjective evaluation of synthesized voice. Although it is desirable to remove workers who do not satisfy the client's requirements, worker filtering criteria have not yet been defined. In this study, we focused on subjective evaluation of sound quality (amount of distortion) and examined filtering criteria. In the filtering test, the comparison task was designed so that attributes other than intonation and sound quality were identical in order to enable evaluation of the ability to distinguish differences in sound quality. In order for the worker to understand the difference in sound quality intuitively, we showed the workers the highly distorted voice several times repeatedly at the beginning of the evaluation. We conducted sound quality evaluation experiments on Amazon Mechanical Turk to investigate the effects of the following filtering criteria on the subjective evaluation results: textit{i)} whether the evaluation was focused on the amount of distortion (Understanding of Intent), textit{ii)} whether the responses were consistent (Response Consistency Rate), textit{iii) }whether the responses were given with confidence (Response Confidence). The results showed that measuring the degree of Understanding of Intentions and Response Confidence is effective in worker selection, and this can be achieved by including a few samples that are useful for Understanding of Intention (in this study, low sound quality voice) in the comparison task.
Keyword (in Japanese) (See Japanese page) 
(in English) crowdsourcing / speech synthesis / worker filtering / subjective evaluation / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 81, SP2022-24, pp. 104-109, June 2022.
Paper # SP2022-24 
Date of Issue 2022-06-10 (SP) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee SP IPSJ-MUS IPSJ-SLP  
Conference Date 2022-06-17 - 2022-06-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2022-06-SP-MUS-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Worker Filtering Criteria for Subjective Evaluation of Synthesized Voice Sound Quality Using Crowdsourcing 
Sub Title (in English)  
Keyword(1) crowdsourcing  
Keyword(2) speech synthesis  
Keyword(3) worker filtering  
Keyword(4) subjective evaluation  
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1st Author's Name Moe Yaegashi  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Susumu Saito  
2nd Author's Affiliation Waseda University/Intelligent Framework Lab Inc. (Waseda Univ./ifLab.)
3rd Author's Name Teppei Nakano  
3rd Author's Affiliation Waseda University/Intelligent Framework Lab Inc. (Waseda Univ./ifLab.)
4th Author's Name Tetsuji Ogawa  
4th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2022-06-18 15:00:00 
Presentation Time 120 minutes 
Registration for SP 
Paper # SP2022-24 
Volume (vol) vol.122 
Number (no) no.81 
Page pp.104-109 
#Pages
Date of Issue 2022-06-10 (SP) 


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