Date: 1 st May,2017

Presenter: Prof. Panayiotis Georgiou, Assistant Professor, University of Southern California (USC)

When Signal Processing, Machine Learning, and Mental Health converge: Communication Understanding and Behavior Analysis

Abstract

​The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Assessment of behavior is also a critical element for behavioral sciences and especially for mental health. Human assessment is often costly, subjective, slow, and biased, especially in atypical cases, which are the ones of greatest interest. Mental health domain experts can inform development of methods that can assess human communication towards behavior analysis. Expert knowledge can, for example, define and guide behaviors and patterns of interest and can provide exemplars. Machine learning also provides unique opportunities towards data-driven assessment. In this talk, I am going to present an overview of this convergence towards analysis of couples’ therapy interactions. I will describe specific examples of knowledge and data-driven machine learning methods that can aid mental health experts. I will discuss a range of directions such as outcome prediction and unsupervised learning.