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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
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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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Cited Papers
A Hybrid Model for Depression Detection With Transformer and Bi-directional Long Short-Term Memory
PREAI
ShareSaveApproximating the Mental Lexicon from Clinical Interviews as a Support Tool for Depression Detection
PREAI
ShareSaveDetecting depression from facial actions and vocal prosody.png)
2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops
2009 3rd International Conference on Affective Computing and Intelligent Interaction and WorkshopsIF0
PREAI
ShareSaveExploiting Vocal Tract Coordination Using Dilated CNNS For Depression Detection In Naturalistic EnvironmentsICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
OAAI
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)IF0
OAAIShareSaveShareSaveShareSave
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