Multi-modal Depression Estimation based on Sub-attentional Fusion

• URL: http://arxiv.org/abs/2207.06180v1
• Date: Wed, 13 Jul 2022 13:19:32 GMT
• Title: Multi-modal Depression Estimation based on Sub-attentional Fusion
• Authors: Ping-Cheng Wei, Kunyu Peng, Alina Roitberg, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen
• Abstract summary: Failure to diagnose depression leads to over 280 million people suffering from this psychological disorder worldwide. We tackle the task of automatically identifying depression from multi-modal data. We introduce a sub-attention mechanism for linking heterogeneous information.
• Score: 29.74171323437029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Failure to timely diagnose and effectively treat depression leads to over 280 million people suffering from this psychological disorder worldwide. The information cues of depression can be harvested from diverse heterogeneous resources, e.g., audio, visual, and textual data, raising demand for new effective multi-modal fusion approaches for its automatic estimation. In this work, we tackle the task of automatically identifying depression from multi-modal data and introduce a sub-attention mechanism for linking heterogeneous information while leveraging Convolutional Bidirectional LSTM as our backbone. To validate this idea, we conduct extensive experiments on the public DAIC-WOZ benchmark for depression assessment featuring different evaluation modes and taking gender-specific biases into account. The proposed model yields effective results with 0.89 precision and 0.70 F1-score in detecting major depression and 4.92 MAE in estimating the severity. Our attention-based fusion module consistently outperforms conventional late fusion approaches and achieves a competitive performance compared to the previously published depression estimation frameworks, while learning to diagnose the disorder end-to-end and relying on far less preprocessing steps.

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