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Depression, a common mental disorder, significantly influences individualsand imposes considerable societal impacts. The complexity and heterogeneity ofthe disorder necessitate prompt and effective detection, which nonetheless,poses a difficult challenge. This situation highlights an urgent requirementfor improved detection methods. Exploiting auditory data through advancedmachine learning paradigms presents promising research directions. Yet,existing techniques mainly rely on single-dimensional feature models,potentially neglecting the abundance of information hidden in various speechcharacteristics. To rectify this, we present the novel Attention-Based AcousticFeature Fusion Network (ABAFnet) for depression detection. ABAFnet combinesfour different acoustic features into a comprehensive deep learning model,thereby effectively integrating and blending multi-tiered features. We presenta novel weight adjustment module for late fusion that boosts performance byefficaciously synthesizing these features. The effectiveness of our approach isconfirmed via extensive validation on two clinical speech databases, CNRAC andCS-NRAC, thereby outperforming previous methods in depression detection andsubtype classification. Further in-depth analysis confirms the key role of eachfeature and highlights the importance of MFCCrelated features in speech-baseddepression detection.

Attention-Based Acoustic Feature Fusion Network for Depression Detection