  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.
