The Interspeech 2025 Speech Accessibility Project Challenge

• URL: http://arxiv.org/abs/2507.22047v1
• Date: Tue, 29 Jul 2025 17:50:59 GMT
• Title: The Interspeech 2025 Speech Accessibility Project Challenge
• Authors: Xiuwen Zheng, Bornali Phukon, Jonghwan Na, Ed Cutrell, Kyu Han, Mark Hasegawa-Johnson, Pan-Pan Jiang, Aadhrik Kuila, Colin Lea, Bob MacDonald, Gautam Mantena, Venkatesh Ravichandran, Leda Sari, Katrin Tomanek, Chang D. Yoo, Chris Zwilling,
• Abstract summary: 2025 Interspeech Speech Accessibility Project launched, utilizing over 400 hours of SAP data collected and transcribed from more than 500 individuals with diverse speech disabilities.<n>12 out of 22 valid teams outperformed the whisper-large-v2 baseline in terms of Word Error Rate and Semantic Score.<n>The top team achieved the lowest WER of 8.11%, and the highest SemScore of 88.44% at the same time, setting new benchmarks for future ASR systems in recognizing impaired speech.
• Score: 35.902086799949345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the last decade has witnessed significant advancements in Automatic Speech Recognition (ASR) systems, performance of these systems for individuals with speech disabilities remains inadequate, partly due to limited public training data. To bridge this gap, the 2025 Interspeech Speech Accessibility Project (SAP) Challenge was launched, utilizing over 400 hours of SAP data collected and transcribed from more than 500 individuals with diverse speech disabilities. Hosted on EvalAI and leveraging the remote evaluation pipeline, the SAP Challenge evaluates submissions based on Word Error Rate and Semantic Score. Consequently, 12 out of 22 valid teams outperformed the whisper-large-v2 baseline in terms of WER, while 17 teams surpassed the baseline on SemScore. Notably, the top team achieved the lowest WER of 8.11\%, and the highest SemScore of 88.44\% at the same time, setting new benchmarks for future ASR systems in recognizing impaired speech.

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