Objective:

To propose a gender-emotion interaction multi-task network (G-EIMTNet) for improved recognition of depression through transformer-based cross-modal fusion.

Approach:

Key Findings:

• The G-EIMTNet achieved a 15.88% improvement in accuracy and a 14.73% improvement in F1 score compared to the baseline model on the AVEC2014 dataset.
• Ablation experiments demonstrated the effectiveness of both multi-modal fusion and the integration of gender-emotion interactions.

Interpretation:

The study emphasizes the integration of gender and emotional factors in depression recognition models.

Limitations:

• The adaptive fusion of heterogeneous features is not yet optimized.
• The integration of gender and emotion-related information within a multi-task framework requires further exploration.

Conclusion:

The G-EIMTNet integrates gender and emotion effectively, addressing some limitations of existing depression recognition methods.

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