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Behavioral Signal Processing: Computational
Approaches for Modeling and Quantifying Interaction
Dynamics in Dyadic Human Interactions
by
Chi-Chun Lee
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
December, 2012
Copyright 2012 Chi-Chun Lee
Object Description
| Title | Behavioral signal processing: computational approaches for modeling and quantifying interaction dynamics in dyadic human interactions |
| Author | Lee, Chi-Chun |
| Author email | jeremylee.cc@gmail.com;chiclee@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-10-18 |
| Date submitted | 2012-11-26 |
| Date approved | 2012-11-26 |
| Restricted until | 2012-11-26 |
| Date published | 2012-11-26 |
| Advisor (committee chair) | Narayanan, Shrikanth S. |
| Advisor (committee member) |
Kuo, Jay (C.-C) Margolin, Gayla |
| 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 human 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 psycho-pathologies such as addiction and depression). In engineering, a metaphorical analogy and framework to this notion in behavioral science is based on the idea of conceptualizing a dyadic interaction as a coupled dynamical system: an interlocutor is viewed as a dynamical system, whose state evolution is not only based on its past history but also dependent on the other interlocutor's state. However, the evolution of this "coupled-states" is often hidden by nature; an interlocutor in a conversation can at best "fully-observe" the expressed behaviors of the other interlocutor. This observation or partial insights into the other interlocutor's state is taken as "input" into the system coupling with the evolution of its own state. This, then, in returns, "outputs" behaviors to be taken as "input" for the other interlocutors. This complex dynamics is in essence capturing the flow of dyadic interaction quantitatively. The challenge in modeling human interactions is, therefore, 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 thesis, I will focus on addressing questions of developing computational methods for carrying out studies into understanding and modeling interaction dynamics in dyadic interactions. In specific, I will first demonstrate the efficacy of jointly model interlocutors behaviors for better prediction of interruption in conversations. Since turn taking is a highly-coordinated behavioral phenomenon between interlocutors, it is beneficial to model both speakers together to achieve better prediction accuracy. Second, I have contributed to the domain of affective computing, recognizing human emotional states through behavioral signals extraction from audio-video recordings, with a hierarchical structure of classfication. Furthermore, with joint modeling of emotional states with DBN, I have demonstrated that it improves over single speaker emotion recognition system. Next, I have developed a computational tool showing the ability of quantifying subtle interaction dynamics for quantifying vocal entrainment, a natural spontaneous vocal behavior matching between interlocutors. The computational tool, with close collaboration with psychologists, was able to bring further insights in the domain of mental health (in specific, distressed married couples) with regard to the cyclical behavior of demand and withdraw. Lastly, I have presented an initial computational approach for studying perceptual process of human observers, viewed as distal interacting entities, in the context of subjective human behavior judgments. Since most studies in behavioral science rely heavily on trained annotators to carry out analysis into human behaviors, given an existing database with multiple annotators ratings, I have designed an initial computational approach to understand the underlying perception mechanism. |
| Keyword | behavioral signal processing; interpersonal interaction; interaction dynamics; dyadic interactions; affective computing; mental health |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Lee, Chi-Chun |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume6/etd-LeeChiChun-1345.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Full text | Behavioral Signal Processing: Computational Approaches for Modeling and Quantifying Interaction Dynamics in Dyadic Human Interactions by Chi-Chun Lee A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) December, 2012 Copyright 2012 Chi-Chun Lee |
