Enhanced Multimodal Depression Detection With Emotion Prompts

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Abstract
Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection.

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