In this study, we focus on automated approaches to detect depression from clinical interviews using multi-modal machine learning (ML). Our approach differentiates from other successful ML methods such as context-aware analysis through feature engineering and end-to-end deep neural networks for depression detection utilizing the Distress Analysis Interview Corpus. We propose a novel method that incorporates: (1) pre-trained Transformer combined with data augmentation based on topic modelling for textual data; and (2) deep 1D convolutional neural network (CNN) for acoustic feature modeling. The simulation results demonstrate the effectiveness of the proposed method for training multi-modal deep learning models. Our deep 1D CNN and Transformer models achieved state-of-the-art performance for audio and text modalities respectively. Combining them in a multi-modal framework also outperforms state-of-the-art for the combined setting. Code available at https://github.com/genandlam/multi-modal-depression-detection
@article{arxiv.2412.19209,
title = {Context-Aware Deep Learning for Multi Modal Depression Detection},
author = {Genevieve Lam and Huang Dongyan and Weisi Lin},
journal= {arXiv preprint arXiv:2412.19209},
year = {2024}
}
Comments
Presented as an Oral at International Conference on Acoustics, Speech and Signal Processing 2019, United Kingdom