ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection
<|reference_start|>ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection: 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.<|reference_end|>
@article{phukan2024comfeat:,
    title={ComFeAT: Combination of Neural and Spectral Features for Improved
  Depression Detection},
    author={Orchid Chetia Phukan, Sarthak Jain, Shubham Singh, Muskaan Singh, Arun
  Balaji Buduru, Rajesh Sharma},
    journal={arXiv preprint arXiv:2406.06774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06774},
    primaryClass={eess.AS cs.SD}
}