Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives
arXiv:2607.03744v1 Announce Type: new Abstract: Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech. However, the interactional timing between the clinician and participant remains comparatively under-modeled. We investigate conversational temporal dynamics, specifically dyadic turn-pair timing, as a primary modality fused with self-supervised encoders. Evaluated on the DAIC-WOZ dataset, we compare a compact 24-dimensional timing module against frozen WavLM-large and RoBERTa-large baseline detectors. T
Advances in AI, particularly in natural language processing and multi-modal analysis, are enabling more sophisticated approaches to mental health diagnostics.
This research could lead to more accurate and accessible early detection of depression, reducing diagnostic delays and improving treatment outcomes.
The diagnostic process for mental health conditions could become more objective and automated, incorporating nuanced interactional data alongside traditional linguistic and acoustic cues.
- · AI healthcare developers
- · Mental health patients
- · Clinical psychology
- · Traditional diagnostic methods
- · Patients with undiagnosed depression (if not adopted)
More timely and accurate depression diagnoses become possible, potentially improving patient prognoses.
The integration of such AI tools could reduce the burden on mental health practitioners while expanding access to diagnostic evaluations.
Ethical considerations around data privacy and bias in AI diagnostics will become increasingly critical as these technologies are deployed widely.
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Read at arXiv cs.AI