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Topic Modeling Based Multi-modal Depression Detection - 2017

Topic Modeling Based Multi-Modal Depression Detection

Research Paper on Topic Modeling Based Multi-Modal Depression Detection

Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Topic Modeling
Multi-Modal
Depression Detection
Audio/Visual Emotion Challenge (AVEC)
Machine Learning
Deep Learning

Author(s) Name:  Yuan Gong , Christian Poellabauer

Journal name:  

Conferrence name:  AVEC -17: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge

Publisher name:  ACM

DOI:  10.1145/3133944.3133945

Volume Information: