Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators
Type
conference poster
Date Issued
2026-03-20
Author(s)
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.
Language
English (United States)
Keywords
Depression detection
acoustic biomarkers
digital health
passive sensing
smart home
HSG Classification
contribution to practical use / society
Refereed
Yes
Publisher
IEEE
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
IEEE_WiP_PerCom_IHearYou-final.pdf
Size
831.48 KB
Format
Adobe PDF
Checksum (MD5)
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