Evaluating Acoustic and Linguistic Features of Detecting Depression Sub-Challenge Dataset


Depression affects hundreds of millions of individuals world wide. With the prevalence of depression increasing, economic costs of the illness are growing significantly. The AVEC 2019 Detecting Depression with AI (Artificial Intelligence) Sub-Challenge provides an opportunity to use novel signal processing, machine learning, and artificial intelligence technology to predict the presence and severity of depression in individuals through digital biomarkers such as vocal acoustics, linguistic contents of speech, and facial expression. In our analysis, we point out key factors to consider during pre-processing and modelling to effectively build voice biomarkers for depression. We additionally verify the dataset for balance in demographic and severity score distribution to evaluate the generalizability of our results.

AVEC ‘19: Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop (ACM Multimedia)
Larry Zhang
Larry Zhang
Dual PhD Student in Complex Network Systems and Intelligent Systems Engineering

My research interests include Behavioral Signal Processing, Cognitive Science, and Affective Computing applied to real world clinical applications in mental health and neuropsychiatry. My hope is to contribute to the development of intelligent interfaces to improve human wellbeing.