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arXiv · 2506.06566

AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition

Chen Bao·Chuanbing Huo·Qinyu Chen·Chang Gao
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Abstract

This paper proposes AS-ASR, a lightweight aphasia-specific speech recognition framework based on Whisper-tiny, tailored for low-resource deployment on edge devices. Our approach introduces a hybrid training strategy that systematically combines standard and aphasic speech at varying ratios, enabling robust generalization, and a GPT-4-based reference enhancement method that refines noisy aphasic transcripts, improving supervision quality. We conduct extensive experiments across multiple data mixing configurations and evaluation settings. Results show that our fine-tuned model significantly outperforms the zero-shot baseline, reducing WER on aphasic speech by over 30% while preserving performance on standard speech. The proposed framework offers a scalable, efficient solution for real-world disordered speech recognition.

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Chen Bao, Chuanbing Huo, Qinyu Chen, Chang Gao. 2025-06-06. AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition. https://doi.org/10.1109/biocas67066.2025.00027

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