2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDMMSP-5.1
Paper Title HCAG: A HIERARCHICAL CONTEXT-AWARE GRAPH ATTENTION MODEL FOR DEPRESSION DETECTION
Authors Meng Niu, Kai Chen, Qingcai Chen, Lufeng Yang, Harbin Institute of Technology, Shenzhen, China
SessionMMSP-5: Human Centric Multimedia 1
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Multimedia Signal Processing: Signal Processing for Multimedia Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Depression is one of the most common mental health disorders, it's crucial to design an effective and robust model for automatic depression detection (ADD). Although current approaches rely on extra topic models or manually topic-selection procedures which is time-consuming, they still haven't thoroughly explored the sufficient context information among clinical interviews. In this paper, we propose HCAG, a novel Hierarchical Context-Aware Graph attention model for ADD. Our model mirrors the hierarchical structure of depression assessment and leverages the Graph Attention Network (GAT) to grasp relational contextual information of text/audio modality. Experiments on the DAIC-WOZ dataset show a great performance improvement, with the F1-score of 0.92, a Mean Absolute Error (MAE) of 2.94, and a Root Mean Square Error (RMSE) of 3.80. To the best of our knowledge, our model outperforms the existing state-of-the-art methods.