A novel teacher-student architecture that combines text and audio data to significantly improve depression classification accuracy.

• Multi-head attention mechanisms enable more effective feature fusion
• Weighted multimodal transfer learning optimizes integration of different data types
• Student fusion model leverages guidance from specialized text and audio teacher models
• DAIC-WOZ dataset validation demonstrates superior performance over traditional approaches

Medical Impact: This multimodal approach offers mental health professionals more reliable diagnostic tools, potentially enabling earlier intervention and improving treatment outcomes for depression patients.

Multimodal Magic: Elevating Depression Detection with a Fusion of Text and Audio Intelligence