An Audio Language Model-Based Voice Concept Bottleneck Framework for Interpretable Health Assessment
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a compelling application of Audio Language Models as interpretable concept bottlenecks for health assessment, demonstrating that decoupling concept extraction from classification can yield both high performance and clinical interpretability, although validation on larger, more diverse datasets is needed to confirm robustness.
Methodology
The paper proposes a novel framework that leverages Audio Language Models (ALMs), specifically Audio Flamingo Next, as independent concept extractors for interpretable health assessment. The core innovation lies in decoupling the concept extraction (via fine-tuned ALM producing discrete 1-5 ordinal scores) from the downstream classification (via XGBoost). This approach aims to mitigate shortcut learning and enhance interpretability by restricting predictions to human-understandable concepts (Clinical Voice Quality and Speaking Behavior). The methodology is sound in its intent, utilizing LoRA for efficient fine-tuning and aggregating speaker-level statistics for the classifier. However, the novelty is somewhat incremental, as concept bottleneck models (CBMs) are an established paradigm, and applying them to audio via LLMs is a logical but not radically new extension. The specific adaptation of ALMs to produce discrete ordinal concepts rather than continuous embeddings or text descriptions is a notable technical choice.
Results & evaluation
The experimental evaluation covers two distinct health conditions: depression (Android Corpus) and dysarthria (TORGO corpus). The results demonstrate that the proposed ALM-based framework outperforms baselines using openSMILE and self-supervised models (Vox-Profile). The paper provides SHAP analysis to validate interpretability, showing that features like pitch variability and speech tempo align with clinical expectations for depression. For dysarthria, the separation of CVQ scores across severity levels is shown. While the performance gains are consistent, the baselines are relatively standard for the domain. The comparison with Vox-Profile is fair, but the lack of comparison with other recent SSL-based CBMs or end-to-end interpretable models limits the comprehensiveness of the benchmark. The results are promising but rely on small datasets, particularly for dysarthria (only 15 speakers total), which raises questions about generalizability.
Reproducibility
The paper provides sufficient detail regarding the model architecture (Audio Flamingo Next), fine-tuning parameters (LoRA rank, alpha, dropout), and downstream classifier settings (XGBoost hyperparameters). The datasets are publicly available, and the voice activity detection tool is cited. However, the code is not explicitly linked in the provided text, and specific random seeds or exact data splitting procedures beyond the standard protocols are not detailed. The reliance on a specific Hugging Face model checkpoint aids reproducibility, but the fine-tuning process on the PVQD dataset requires careful handling of the discrete label generation which is described but could be more precise.
Limitations
The primary limitation is the small sample size of the evaluation datasets, especially the dysarthria corpus, which limits the statistical power of the results. The ALM's pretraining bias is acknowledged and partially corrected via fine-tuning, but the paper notes that the model still struggles with certain concepts like abnormal loudness and pitch due to definition mismatches. The discrete 1-5 scale, while interpretable, may lose nuance compared to continuous ratings. Furthermore, the "independent" nature of the concept extractor assumes that the fine-tuned ALM captures the true underlying concepts without leakage from the downstream task, which is hard to guarantee without rigorous ablation studies on concept fidelity.
Broader impact
This work has significant potential impact in clinical decision support systems by providing interpretable voice biomarkers. By making AI predictions transparent through human-understandable concepts, it can help clinicians trust and validate AI outputs. The framework could be extended to other health conditions affecting voice, such as Parkinson's disease or Alzheimer's. However, ethical considerations regarding privacy, bias in health assessment, and the risk of over-reliance on automated diagnostics must be addressed. The use of ALMs also raises questions about the environmental cost and accessibility of such models in resource-constrained clinical settings. This paper presents a compelling application of Audio Language Models as interpretable concept bottlenecks for health assessment, demonstrating that decoupling concept extraction from classification can yield both high performance and clinical interpretability, although validation on larger, more diverse datasets is needed to confirm robustness.
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