# Context-Aware Deep Learning for Multi Modal Depression Detection

arXiv: https://arxiv.org/abs/2412.19209 · HTML: https://mlsanity.com/paper/2412.19209/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)

## Built on by (most cited first, 4 of 4)

- [Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration](https://mlsanity.com/paper/2502.12204/) (2025)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech](https://mlsanity.com/paper/2510.08593/) (2025)
- [Tri-Modal Severity Fused Diagnosis across Depression and Post-traumatic Stress Disorders](https://mlsanity.com/paper/2510.20239/) (2025)

## Abstract (arXiv)

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