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Speech Comm Lab Seminar: "Computational Approaches for Modeling and Quantifying Human Interaction"
Monday, November 19, 2012
4:30 p.m.
1146 A.V. Williams Building
For More Information:
Carol Espy-Wilson
espy@umd.edu

The Speech Communication Lab Seminar Series

Computational Approaches for Modeling and Quantifying Human Interaction Dynamics

Jeremy Lee
Ph.D. Candidate
Electrical Engineering Department
University of Southern California

Abstract
Behavioral Signal Processing (BSP) is an emerging interdisciplinary research domain, operationally defined as computational methods that model human behavior signals, with a goal of enhancing the capabilities of domain experts in facilitating better decision making in terms of both scientific discovery in human behavioral sciences and human-centered system designs. Quantitative understanding of human behavior, both typical and atypical, and mathematical modeling of interaction dynamics are core elements in BSP. This thesis focuses on computational approaches in modeling and quantifying interacting dynamics in dyadic interactions.

The study of interaction dynamics has long been at the center for multiple research disciplines in human behavioral sciences (e.g., psychology). Exemplary scientific questions addressed range from studying scenarios of interpersonal communication (verbal interaction modeling, human affective state generation, display, and perception mechanisms), modeling domain-specific interactions (such as, assessment of the quality of theatrical acting or children’s reading ability), to analyzing atypical interactions (for example, models of distressed married couples behavior and response to therapeutic interventions, quantitative diagnostics and treatment tracking of children with Autism, people with psychopathologies such as addiction and depression).

The challenge in modeling human interactions is multi-fold: the coupling dynamic between each interlocutor in an interaction spans multiple levels, along variable time scales, and differs between interaction contexts. At the same time, each interlocutor’s internal behavioral dynamic produces a coupling that is multimodal across the verbal and nonverbal communicative channels. In this talk, I will demonstrate the efficacy of jointly model interlocutors’ behaviors for better recognition of affective and turn taking dynamics, showcase the ability of quantifying subtle interaction dynamics as computational tools for gaining further insights in the domain of mental health (in specific, distressed married couples), and finally present a computational approach for studying perceptual process of human observers, viewed as distal interacting entities, in the context of subjective human behavior judgments.

Biography
Jeremy (Chi-Chun) Lee received the B.S. degree with honor, Magna Cum Laude, in Electrical Engineering with a minor in Business Administration from the University of Southern California (USC), Los Angeles, California in 2007. He is currently a Ph.D. candidate in Electrical Engineering Department and a member of Signal Analysis and Interpretation Laboratory (SAIL).

His research interests are in Behavioral Signal Processing (BSP), emphasizing the development of computational frameworks in recognizing and quantifying human behavioral attributes and interpersonal interaction dynamics using machine learning and signal processing techniques. He has been involved in multiple interdisciplinary research projects and has conducted collaborative research across domains of behavioral sciences. He was awarded with the USC Annenberg Fellowship (2007 - 2009). He also led a team in SAIL to participate and win the Emotion Challenge - Classifier SubChallenge in 2009 (10th Annual Conference of the International Speech Communication Association).

This Event is For: Graduate • Undergraduate • Faculty • Post-Docs • Alumni

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