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arXiv · 1711.11155

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

Aven Samareh·Yan Jin·Zhangyang Wang·Xiangyu Chang·Shuai Huang
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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 AVEC 2017 data set, our proposed model outperforms each single modality prediction model, and surpasses the data set baseline with ice margin.

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Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, Shuai Huang. 2017-11-29. Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion. https://arxiv.org/abs/1711.11155

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