Context-Aware Deep Learning for Multi Modal Depression Detection

Genevieve Lam, Huang Dongyan, Weisi Lin
12/26/2024
cs.LG

Official code: genandlam/multi-modal-depression-detection (11 stars, MIT, last commit 2024-12-26).

Abstract

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

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Code Implementations(1)

genandlam/multi-modal-depression-detectionOfficial100%
MIT

Official codebase for "Context Aware Deep Learning for Multi Modal Depression Detection" [ICASSP 2019, Oral]

110Python, Jupyter NotebookDec 26, 20241 years agoMIT
cnndeep-learningdepression-detectionmulti-modal-learningtransformer

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Cite this paper

@article{lam2024contextaware,
  title  = {Context-Aware Deep Learning for Multi Modal Depression Detection},
  author = {Genevieve Lam and Huang Dongyan and Weisi Lin},
  year   = {2024},
  eprint = {2412.19209},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.19209v1}
}

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