Context-aware Deep Learning for Multi-modal Depression Detection

Genevieve Lam · Weisi Lin

129 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Sentiment Analysis and Opinion Mining Mental Health via Writing

Abstract

In this study, we focus on automated approaches to detect depression from clinical interviews using machine learning approached, which the models are trained on multi-modal data. Differentiating from successful machine learning approaches such as context-aware analysis through feature engineering and end-to-end deep neural networks to depression detection utilizing the Distress Analysis Interview Corpus, we propose a novel method that incorporates a data augmentation procedure based on topic modelling using transformer and 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 achieve the state-of-the-art performance for the audio and text modalities respectively, while our multi-modal results are comparable with the state-of-the-art depression detection systems.

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References

2010Validation of PHQ-2 and PHQ-9 to Screen for Major Depression in the Primary Care Population 2014The Distress Analysis Interview Corpus of human and computer interviews 2018Detecting Depression with Audio/Text Sequence Modeling of Interviews 2016DepAudioNet 2016Detecting Depression using Vocal, Facial and Semantic Communication Cues 2006Should we screen for depression?2017Multimodal Measurement of Depression Using Deep Learning Models 2016Decision Tree Based Depression Classification from Audio Video and Language Information

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2021Multi-modal depression detection based on emotional audio and evaluation text 2020Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model 2023Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review 2022Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data 2021HCAG: A Hierarchical Context-Aware Graph Attention Model for Depression Detection

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