AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911415278764032 |
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| author | Bao, Chen Huo, Chuanbing Chen, Qinyu Gao, Chang |
| author_facet | Bao, Chen Huo, Chuanbing Chen, Qinyu Gao, Chang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06566 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition Bao, Chen Huo, Chuanbing Chen, Qinyu Gao, Chang Audio and Speech Processing Artificial Intelligence 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. |
| title | AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition |
| topic | Audio and Speech Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2506.06566 |