Context-Aware Deep Learning for Multi Modal Depression Detection

• URL: http://arxiv.org/abs/2412.19209v1
• Date: Thu, 26 Dec 2024 13:19:26 GMT
• Title: Context-Aware Deep Learning for Multi Modal Depression Detection
• Authors: Genevieve Lam, Huang Dongyan, Weisi Lin,
• Abstract summary: We focus on automated approaches to detect depression from clinical interviews using multi-modal machine learning (ML) 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. Our deep 1D CNN and Transformer models achieved state-of-the-art performance for audio and text modalities respectively.
• Score: 41.02897689721331
• License:
• 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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