Cross-Language Depression Detection Based on Multi-Domain Feature Alignment

Deep learning has shown strong potential for detecting depression from speech. However, speaker-specific traits and language differences can cause feature shifts, limiting the model's ability to generalize across individuals and languages. To overcome this challenge, we propose a Multi-Domain Feature Alignment (MDFA) model that minimizes the impact of individual and language differences on depression-related features. Using gradient reversal techniques, our approach reduces the feature encoder's sensitivity to speaker and language-specific traits, allowing the model to focus on depression-related patterns for more effective classification. Experimental results on datasets from different languages, including DAIC-WoZ and Androids, demonstrate that the MDFA model significantly improves generalization across individuals and enhances performance in cross-language tasks.