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 classification. 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.
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