  Depression, a common mental disorder, significantly influences individuals
and imposes considerable societal impacts. The complexity and heterogeneity of
the disorder necessitate prompt and effective detection, which nonetheless,
poses a difficult challenge. This situation highlights an urgent requirement
for improved detection methods. Exploiting auditory data through advanced
machine 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 speech
characteristics. To rectify this, we present the novel Attention-Based Acoustic
Feature Fusion Network (ABAFnet) for depression detection. ABAFnet combines
four different acoustic features into a comprehensive deep learning model,
thereby effectively integrating and blending multi-tiered features. We present
a novel weight adjustment module for late fusion that boosts performance by
efficaciously synthesizing these features. The effectiveness of our approach is
confirmed via extensive validation on two clinical speech databases, CNRAC and
CS-NRAC, thereby outperforming previous methods in depression detection and
subtype classification. Further in-depth analysis confirms the key role of each
feature and highlights the importance of MFCCrelated features in speech-based
depression detection.
