Major depressive disorder is a common mental disorder that affects almost 7% of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC) asks participants to build a model to predict depression levels based on the audio, video, and text of an interview ranging between 7-33 minutes. Since averaging features over the entire interview will lose most temporal information, how to discover, capture, and preserve useful temporal details for such a long interview are significant challenges. Therefore, we propose a novel topic modeling based approach to perform context-aware analysis of the recording. Our experiments show that the proposed approach outperforms context-unaware methods and the challenge baselines for all metrics.
@article{arxiv.1803.10384,
title = {Topic Modeling Based Multi-modal Depression Detection},
author = {Yuan Gong and Christian Poellabauer},
journal= {arXiv preprint arXiv:1803.10384},
year = {2018}
}
Comments
Proceedings of the 7th Audio/Visual Emotion Challenge and Workshop (AVEC). (Official Depression Challenge Winner)