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Paper Abstract and Keywords
Presentation 2024-03-01 16:05
Evaluating speech generation based on objective measures for text generation
Takaaki Saeki (UTokyo), Soumi Maiti (CMU), Shinnosuke Takamichi (UTokyo), Shinji Watanabe (CMU), Hiroshi Saruwatari (UTokyo) EA2023-133 SIP2023-180 SP2023-115
Abstract (in Japanese) (See Japanese page) 
(in English) In the evaluation of speech generation, while subjective judgments have long been the gold standard, objective metrics such as Mel Cepstral Distortion (MCD) and Mean Opinion Score (MOS) prediction models have also been used. These objective metrics are valued for their lower time and financial costs and the ability to compare different results, driving the demand for metrics that correlate well with human subjective judgments. This paper proposes an automatic evaluation method for speech generation based on text generation metrics. Our proposed SpeechBERTScore computes BERTScores on self-supervised speech feature sequences derived from both generated and reference speech. In addition, SpeechBLEU and SpeechTokenDistance define metrics using self-supervised discrete speech tokens. Experimental evaluations of synthesized speech show that our SpeechBERTScore correlates better with human subjective ratings than MCD and state-of-the-art MOS prediction models. Furthermore, our approach is effective for degraded speech evaluation and applicable across languages.
Keyword (in Japanese) (See Japanese page) 
(in English) Automatic speech evaluation / Speech generation / Self-supervised speech representations / Text generation metrics / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 403, SP2023-115, pp. 421-426, Feb. 2024.
Paper # SP2023-115 
Date of Issue 2024-02-22 (EA, SIP, 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)
Download PDF EA2023-133 SIP2023-180 SP2023-115

Conference Information
Committee SIP SP EA IPSJ-SLP  
Conference Date 2024-02-29 - 2024-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2024-02-SIP-SP-EA-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluating speech generation based on objective measures for text generation 
Sub Title (in English)  
Keyword(1) Automatic speech evaluation  
Keyword(2) Speech generation  
Keyword(3) Self-supervised speech representations  
Keyword(4) Text generation metrics  
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Keyword(6)  
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1st Author's Name Takaaki Saeki  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Soumi Maiti  
2nd Author's Affiliation Carnegie Mellon University (CMU)
3rd Author's Name Shinnosuke Takamichi  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Shinji Watanabe  
4th Author's Affiliation Carnegie Mellon University (CMU)
5th Author's Name Hiroshi Saruwatari  
5th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2024-03-01 16:05:00 
Presentation Time 20 minutes 
Registration for SP 
Paper # EA2023-133, SIP2023-180, SP2023-115 
Volume (vol) vol.123 
Number (no) no.401(EA), no.402(SIP), no.403(SP) 
Page pp.421-426 
#Pages
Date of Issue 2024-02-22 (EA, SIP, SP) 


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