Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

Authors

  • Aven Samareh University of Washington
  • Yan Jin University of Washington
  • Zhangyang Wang Texas A&M University
  • Xiangyu Chang Xi'an Jiaotong University
  • Shuai Huang University of Washington

Abstract

We present our preliminary work to determine if patient's vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8). We proposed a multi-modal fusion model that combines three different modalities: audio, video, and text features. By training over the AVEC2017 dataset, our proposed model outperforms each single-modality prediction model, and surpasses the dataset baseline with a nice margin.

Downloads

Published

2018-04-29

How to Cite

Samareh, A., Jin, Y., Wang, Z., Chang, X., & Huang, S. (2018). Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12152