Context Aware Deep Learning for Multi Modal Depression Detection

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• Genevieve Lam (Independent Researcher)
• Friday 18 July 2025, 17:00-17:30
• Computer Laboratory, William Gates Building, Room FW26..

If you have a question about this talk, please contact Pietro Lio.

In this talk, 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 modeling 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.

This work was previously presented as an Oral at International Conference on Acoustics, Speech and Signal Processing 2019, United Kingdom

This talk is part of the Data Science and AI in Medicine series.

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• Computer Laboratory, William Gates Building, Room FW26.

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