Multi-modality Depression Detection via Multi-scale Temporal Dilated CNNs

Depression, a prevalent mental illness, is negatively impacting on individual and society. This paper targets the Depression Detection Challenge with AI Sub-challenge (DDS) task of Audio Visual Emotion Challenge (AVEC) 2019. Firstly, two task-specific features are proposed: 1) deep contextual text features, which incorporate global text features and sentiment scores estimated by fine-tuned Bidirectional Encoder Representations from Transformers (BERT); 2) span-wise dense temporal statistical features, in which multiple statistical functions are conducted in each continuous time span. Furthermore, we propose a multi-scale temporal dilated CNN to precisely capture the hidden temporal dependency in the data for automatic multi-modality depression detection. Our proposed framework achieves competitive performance with Concordance Correlation Coefficient (CCC) of 0.466 on development set and 0.430 on test set which is remarkably higher than the baseline result of 0.269 on development set and 0.120 on test set.