ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection

TL;DR

This paper introduces ComFeAT, a novel approach combining neural and spectral speech features with CNNs to improve depression detection accuracy, especially in real-world scenarios, outperforming previous state-of-the-art methods.

Contribution

The paper presents a new feature combination method, ComFeAT, that enhances depression detection by integrating neural and spectral features, demonstrating improved performance over existing approaches.

Findings

Combining neural and spectral features improves depression detection accuracy.

Spectral features provide robustness to domain variations.

The method outperforms previous state-of-the-art on the E-DAIC benchmark.

Abstract

In this work, we focus on the detection of depression through speech analysis. Previous research has widely explored features extracted from pre-trained models (PTMs) primarily trained for paralinguistic tasks. Although these features have led to sufficient advances in speech-based depression detection, their performance declines in real-world settings. To address this, in this paper, we introduce ComFeAT, an application that employs a CNN model trained on a combination of features extracted from PTMs, a.k.a. neural features and spectral features to enhance depression detection. Spectral features are robust to domain variations, but, they are not as good as neural features in performance, suprisingly, combining them shows complementary behavior and improves over both neural and spectral features individually. The proposed method also improves over previous state-of-the-art (SOTA) works on E-DAIC benchmark.