用语音特征解释抑郁症状，让诊断更透明可懂。
Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators
- 构建声学特征与临床指标的显式映射框架
- 在DAIC-WOZ数据集上验证了特征与症状的一致性
- 本地运行保障隐私，适合医疗场景落地
抑郁症影响全球数百万人，但诊断依赖主观自述，可能遗漏真实行为表现。本文提出一种透明的关联框架，将语音声学特征（如音高变化、停顿、语速）与DSM-5抑郁行为指标进行显式关联，实现可解释的指标级输出。系统可在普通硬件上本地运行，保障用户隐私。在DAIC-WOZ数据集上的初步评估显示，声学特征与心理运动改变及注意力困难等DSM-5指标呈现方向一致的关联，验证了设计合理性。未来工作将基于纵向数据集进一步验证，并在保持边缘计算约束的前提下拓展多模态融合。
原文摘要 · Abstract (English)
Depression affects millions worldwide, yet diagnosis relies on subjective self-reports that may miss authentic behavior. This paper presents an approach linking speech acoustics to DSM-5 depressive-behavior indicators through a transparent Linkage Framework. Unlike black-box models, the framework explicitly maps acoustic features (pitch variability, pauses, speech tempo) to clinical indicators, enabling interpretable, indicator-level outputs. The system runs locally on commodity hardware (HW) to preserve privacy. Preliminary evaluation on DAIC-WOZ shows directionally consistent associations between acoustic features and DSM-5 indicators for psychomotor change and concentration difficulty, supporting the design rationale. Future work will validate on longitudinal datasets and extend multimodal integration while maintaining edge constraints.
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