Abstract

Depression is one of the most common mood disorders. Technology has the potential to assist in screening and treating people with depression by robustly modeling and tracking the complex behavioral cues associated with the disorder (e.g., speech, language, facial expressions, head movement, body language). Similarly, robust affect recognition is another challenge which stands to benefit from modeling such cues. The Audio/Visual Emotion Challenge (AVEC) aims toward understanding the two phenomena and modeling their correlation with observable cues across several modalities. In this paper, we use multimodal signal processing methodologies to address the two problems using data from human-computer interactions. We develop separate systems for predicting depression levels and affective dimensions, experimenting with several methods for combining the multimodal information. The proposed …

Date

2014

Authors

Rahul Gupta, Nikolaos Malandrakis, Bo Xiao, Tanaya Guha, Maarten Van Segbroeck, Matthew Black, Alexandros Potamianos, Shrikanth Narayanan

Book

Proceedings of the 4th international workshop on audio/visual emotion challenge

Pages

33-40