Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

2018-02-08

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Student Abstract Track

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.

10.1609/aaai.v32i1.12152