VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916970593517568 |
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| author | Kim, Yubin Kim, Taehan Kang, Wonjune Park, Eugene Yoon, Joonsik Lee, Dongjae Liu, Xin McDuff, Daniel Lee, Hyeonhoon Breazeal, Cynthia Park, Hae Won |
| author_facet | Kim, Yubin Kim, Taehan Kang, Wonjune Park, Eugene Yoon, Joonsik Lee, Dongjae Liu, Xin McDuff, Daniel Lee, Hyeonhoon Breazeal, Cynthia Park, Hae Won |
| contents | Vocal health plays a crucial role in peoples' lives, significantly impacting their communicative abilities and interactions. However, despite the global prevalence of voice disorders, many lack access to convenient diagnosis and treatment. This paper introduces VocalAgent, an audio large language model (LLM) to address these challenges through vocal health diagnosis. We leverage Qwen-Audio-Chat fine-tuned on three datasets collected in-situ from hospital patients, and present a multifaceted evaluation framework encompassing a safety assessment to mitigate diagnostic biases, cross-lingual performance analysis, and modality ablation studies. VocalAgent demonstrates superior accuracy on voice disorder classification compared to state-of-the-art baselines. Its LLM-based method offers a scalable solution for broader adoption of health diagnostics, while underscoring the importance of ethical and technical validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation Kim, Yubin Kim, Taehan Kang, Wonjune Park, Eugene Yoon, Joonsik Lee, Dongjae Liu, Xin McDuff, Daniel Lee, Hyeonhoon Breazeal, Cynthia Park, Hae Won Sound Artificial Intelligence Audio and Speech Processing Vocal health plays a crucial role in peoples' lives, significantly impacting their communicative abilities and interactions. However, despite the global prevalence of voice disorders, many lack access to convenient diagnosis and treatment. This paper introduces VocalAgent, an audio large language model (LLM) to address these challenges through vocal health diagnosis. We leverage Qwen-Audio-Chat fine-tuned on three datasets collected in-situ from hospital patients, and present a multifaceted evaluation framework encompassing a safety assessment to mitigate diagnostic biases, cross-lingual performance analysis, and modality ablation studies. VocalAgent demonstrates superior accuracy on voice disorder classification compared to state-of-the-art baselines. Its LLM-based method offers a scalable solution for broader adoption of health diagnostics, while underscoring the importance of ethical and technical validation. |
| title | VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.13577 |