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• W2935705121 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." @default.
• W2935705121 created "2019-04-25" @default.
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• W2935705121 date "2019-05-01" @default.
• W2935705121 modified "2023-10-11" @default.
• W2935705121 title "Context-aware Deep Learning for Multi-modal Depression Detection" @default.
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• W2935705121 doi "https://doi.org/10.1109/icassp.2019.8683027" @default.
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