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MSTDD: A Multi-scale Transformer Framework for Automatic Depression Detection
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DOI：10.1007/978-981-95-3456-2_13.png)
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Abstract
En 中文
Automatic Depression Detection (ADD) methods utilize multimodal data, including text, audio, and visual information, to facilitate early clinical diagnosis and intervention. However, current ADD approaches predominantly model global or single-scale features, thus inadequately capturing fine-grained local depressive cues and insufficiently exploiting complementary information across modalities. In this paper, we propose MSTDD, a Multi-Scale Transformer-based method designed to address these limitations by effectively extracting and integrating depression-related features at multiple scales. Specifically, MSTDD employs modality-specific multi-scale encoders to capture hierarchical local depressive indicators, and introduces a multimodal cross-attention fusion mechanism to promote robust feature interaction between modalities. Extensive comparative evaluations and ablation experiments conducted on two benchmark depression datasets-DAIC-WOZ (AVEC 2017) and E-DAIC (AVEC 2019)-demonstrate that MSTDD outperforms state-of-the-art ADD methods, achieving average F1-scores of 0.82 on DAIC-WOZ and 0.80 on E-DAIC. Additionally, we conduct experiments comparing baseline models under different fusion strategies, further validating the effectiveness of our proposed method.
Keywords:
Automatic Depression Detection
Multi-Scale Transformer
Multimodal Fusion
Depression Detection
Cross-Attention
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