Attention-Based Acoustic Feature Fusion Network for Depression Detection

TL;DR

This paper introduces ABAFnet, an innovative deep learning model that fuses multiple acoustic speech features using an attention mechanism to improve depression detection accuracy from clinical speech data.

Contribution

The study proposes a novel attention-based acoustic feature fusion network with a weight adjustment module for enhanced depression detection performance.

Findings

Outperforms previous methods on clinical speech databases

Highlights the importance of MFCC features in depression detection

Effective integration of multi-tiered acoustic features

Abstract

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.