Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators
Summary
The paper introduces a transparent framework that maps acoustic speech features to DSM-5 depression indicators for interpretable detection, running locally on commodity hardware to preserve privacy.
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# Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators Source: [https://arxiv.org/abs/2608.26148](https://arxiv.org/abs/2608.26148) [View PDF](https://arxiv.org/pdf/2608.26148) > Abstract: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\. ## Submission history From: Bruno Rodrigues \[[view email](https://arxiv.org/show-email/0dea4177/2608.26148)\] **\[v1\]**Mon, 29 Jun 2026 07:17:46 UTC \(853 KB\)
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