Modeling Conversational Dynamics as a Mixed-Memory Markov Process

There is a long history of work in the social sciences aimed at understanding the interactions between individuals and the influences they have on each others' behavior. However, existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained, or have been forced to rely on questionnaires or diaries to get data on face-to-face interactions. Survey-based methods are error prone and impractical to scale up. Studies show that self-reports correspond poorly to communication behavior as recorded by independent observers [3]. In contrast, we have used wearable sensors and recent advances in speech processing techniques to automatically gather information about conversations: when they occurred, who was involved, and who was speaking when.