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A Multi-Modal Hierarchical Recurrent Neural Network for Depression Detection - 2019

A Multi-Modal Hierarchical Recurrent Neural Network For Depression Detection

Research Area:  Machine Learning


We propose a multi-modal method with a hierarchical recurrent neural structure to integrate vision, audio and text features for depression detection. Such a method contains two hierarchies of bidirectional long short term memories to fuse multi-modal features and predict the severity of depression. An adaptive sample weighting mechanism is introduced to adapt to the diversity of training samples. Experiments on the testing set of a depression detection challenge demonstrate the effectiveness of the proposed method.


Author(s) Name:  Shi Yin , Cong Liang , Heyan Ding , Shangfei Wang

Journal name:  

Conferrence name:  AVEC -19: Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop

Publisher name:  ACM

DOI:  10.1145/3347320.3357696

Volume Information: