发表机构
University of St. Gallen HSG; University of Zurich UZH（圣加仑大学; 苏黎世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出透明关联框架，将语音声学特征映射到DSM-5抑郁指标，在DAIC-WOZ上初步验证了声学特征与相关指标的关联，实现可解释且隐私保护的抑郁检测。
AI 中文摘要
抑郁症影响全球数百万人，但诊断依赖可能遗漏真实行为的主观自我报告。本文提出一种通过透明关联框架将语音声学特征与DSM-5抑郁行为指标关联的方法。与黑箱模型不同，该框架明确将声学特征（音高变异性、停顿、语速）映射到临床指标，实现可解释的指标级输出。系统在商用硬件（HW）上本地运行以保护隐私。对DAIC-WOZ的初步评估显示，声学特征与精神运动变化、注意力困难的DSM-5指标存在方向一致的关联，支持设计原理。未来工作将在纵向数据集上验证，扩展多模态集成同时保持边缘约束。
英文摘要
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.
