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Paper Abstract and Keywords
Presentation 2026-06-06 13:00
Multi-Session EEG-to-Speech Decoding Combining Contrastive Learning with Variational Regularization
Tomoaki Mizuno, Toru Nakashika (UEC) SP2026-24
Abstract (in Japanese) (See Japanese page) 
(in English) Reconstructing perceived speech from non-invasive electroencephalography (EEG) is challenging due to the low signal-to-noise ratio (SNR) and inter-session variability. While trial averaging improves SNR, its application to continuous speech remains difficult. Therefore, we leverage multiple EEG recordings as semantically aligned positive pairs for contrastive learning. Specifically, we propose two conditions: a session-invariant condition to learn representations invariant to session factors, and a speaker-invariant condition to learn representations invariant to speaker factors. We further incorporate a variational regularization term that, rather than constructing a latent variable, acts as an indirect constraint to stabilize the encoder output. Experiments on a Japanese EEG dataset show that combining contrastive learning with variational regularization expands the encoder representation space and improves reconstruction performance. Moreover, the choice of positive pair strategy selectively improves different aspects: the session-invariant condition enhances linguistic fidelity, whereas the speaker-invariant condition improves speaker characteristics.
Keyword (in Japanese) (See Japanese page) 
(in English) Electroencephalography / Speech Synthesis / Auditory Decoding / Deep Learning / Brain-Machine Interface / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 58, SP2026-24, pp. 130-135, June 2026.
Paper # SP2026-24 
Date of Issue 2026-05-29 (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 SP2026-24

Conference Information
Committee SP IPSJ-SLP IPSJ-MUS  
Conference Date 2026-06-05 - 2026-06-06 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Electro-Communications 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2026-06-SP-SLP-MUS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-Session EEG-to-Speech Decoding Combining Contrastive Learning with Variational Regularization 
Sub Title (in English)  
Keyword(1) Electroencephalography  
Keyword(2) Speech Synthesis  
Keyword(3) Auditory Decoding  
Keyword(4) Deep Learning  
Keyword(5) Brain-Machine Interface  
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Keyword(8)  
1st Author's Name Tomoaki Mizuno  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Toru Nakashika  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2026-06-06 13:00:00 
Presentation Time 180 minutes 
Registration for SP 
Paper # SP2026-24 
Volume (vol) vol.126 
Number (no) no.58 
Page pp.130-135 
#Pages 6 
Date of Issue 2026-05-29 (SP) 

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