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arXiv Computation and Language By Jonas L\"anzlinger, Katharina O. E. M\"uller, Burkhard Stiller, Bruno Rodrigues

Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators

Read the original on arXiv Computation and Language →

The paper proposes a transparent framework that links speech acoustic features—such as pitch variability, pauses, and speech tempo—to DSM‑5 indicators of depression, offering interpretable, indicator‑level outputs instead of opaque black‑box models. It runs locally on commodity hardware to preserve privacy and has been preliminarily evaluated on the DAIC‑WOZ dataset, showing consistent associations between acoustic cues and DSM‑5 indicators of psychomotor change and concentration difficulty. Future work aims to validate the approach on longitudinal data and expand multimodal integration while keeping edge constraints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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