Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives
📰 ArXiv cs.AI
Learn how conversational temporal dynamics can improve depression detection in dyads through a multi-modality approach, leveraging turn-pair timing and self-supervised encoders
Action Steps
- Extract conversational temporal dynamics from clinical interview data using turn-pair timing analysis
- Fuse these dynamics with self-supervised encoders to create a multi-modality model
- Evaluate the model on a dataset like DAIC-WOZ to assess its effectiveness in depression detection
- Compare the results with traditional models that only consider semantic content and acoustic characteristics
- Apply the findings to develop more accurate and comprehensive depression detection systems
Who Needs to Know This
This research benefits data scientists, AI engineers, and clinicians working on mental health projects, as it explores a novel approach to depression detection
Key Insight
💡 Conversational temporal dynamics, such as turn-pair timing, can be a valuable modality for improving depression detection in dyads when combined with self-supervised encoders
Share This
💡 Conversational temporal dynamics can enhance depression detection in dyads! 🤖💬 #AIforMentalHealth #DepressionDetection
Key Takeaways
Learn how conversational temporal dynamics can improve depression detection in dyads through a multi-modality approach, leveraging turn-pair timing and self-supervised encoders
Full Article
Title: Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives
Abstract:
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 com
Abstract:
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 com
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