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Paper Abstract and Keywords
Presentation 2026-05-23 09:35
Analysis of Transcription Characteristics of Automatic Speech Recognition for Read-Aloud Speech by Elderly Speakers
Reiya Uchiyama, Minoru Nakayama (Meiji Univ.) HCS2026-20 HIP2026-20
Abstract (in Japanese) (See Japanese page) 
(in English) Character and word error rates are commonly used metrics for evaluating automatic speech recognition systems; however, their reliability for Japanese speech is limited by orthographic variation and homonyms, necessitating human assessment. Nevertheless, human assessment is inherently constrained in the number of trials it can accommodate. In prior work, the paired comparison method for transcribed short texts was applied to young speakers’ speech, demonstrating that human preference judgments could potentially be replaced by the contextual understanding capability of a Large Language Model (LLM). In this paper, we applied this method to elderly speakers’ speech and examined the differences from young speakers’ speech. First, we compared the distributions of character error rates (CER) calculated from the outputs of three speech recognition models for both elderly and young speakers’ speech, and evaluated the recognition characteristics of ASR for elderly speakers’ speech. Additionally, five participants evaluated 50 pairs of ASR-transcribed texts from elderly speakers’ speech, and the issues in ASR were examined. Based on these results, the differences in assessment characteristics between elderly and young speakers’ speech were discussed.
Keyword (in Japanese) (See Japanese page) 
(in English) transcription / elderly speakers’ speech / paired comparison / Large Language Model / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 39, HIP2026-20, pp. 101-106, May 2026.
Paper # HIP2026-20 
Date of Issue 2026-05-15 (HCS, HIP) 
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 HCS2026-20 HIP2026-20

Conference Information
Committee HIP HCS HI-SIGCOASTER  
Conference Date 2026-05-22 - 2026-05-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Communication Enhancing, Human Communications, and Human Information Processing 
Paper Information
Registration To HIP 
Conference Code 2026-05-HIP-HCS-SIGCOASTER 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Transcription Characteristics of Automatic Speech Recognition for Read-Aloud Speech by Elderly Speakers 
Sub Title (in English)  
Keyword(1) transcription  
Keyword(2) elderly speakers’ speech  
Keyword(3) paired comparison  
Keyword(4) Large Language Model  
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1st Author's Name Reiya Uchiyama  
1st Author's Affiliation Meiji University (Meiji Univ.)
2nd Author's Name Minoru Nakayama  
2nd Author's Affiliation Meiji University (Meiji Univ.)
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Speaker Author-1 
Date Time 2026-05-23 09:35:00 
Presentation Time 20 minutes 
Registration for HIP 
Paper # HCS2026-20, HIP2026-20 
Volume (vol) vol.126 
Number (no) no.38(HCS), no.39(HIP) 
Page pp.101-106 
#Pages 6 
Date of Issue 2026-05-15 (HCS, HIP) 

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