An Audio Language Model-Based Voice Concept Bottleneck Framework for Interpretable Health Assessment

Yu-Wen Chen; Julia Hirschberg

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arXiv · 2607.16967

An Audio Language Model-Based Voice Concept Bottleneck Framework for Interpretable Health Assessment

Yu-Wen Chen·Julia Hirschberg

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Abstract

Interpretability is critical in clinical decision support. Concept bottleneck frameworks improve it by representing inputs as human-understandable concepts and restricting predictions solely on them. However, research on their use for voice-based health assessment remains limited. In this study, we propose a voice concept bottleneck framework for interpretable health assessment using an audio language model (ALM). The ALM is fine-tuned on a voice quality assessment dataset to enhance its understanding of voice concepts and serves as an independent concept extractor, producing discrete, interpretable scores for a lightweight downstream classifier. The discrete concept scores provide intuitive interpretation, while the lightweight classifier facilitates post-hoc interpretability analyses. Results on depression and dysarthria assessment tasks demonstrate that the proposed framework can flexibly adapt voice concepts to different health conditions and consistently outperforms openSMILE-based and self-supervised speech model-based baselines.

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BibTeX RIS

Yu-Wen Chen, Julia Hirschberg. 2026-07-18. An Audio Language Model-Based Voice Concept Bottleneck Framework for Interpretable Health Assessment. https://arxiv.org/abs/2607.16967

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