Modeling Behavioral Manifestations of Coordination and Rapport over Multiple Conversations - Speaking Rate as a Relational Indicator for a Virtual Agent

Many potential applications of virtual agents require an agent to conduct multiple conversations with users. An effective and engaging agent should modify its behavior in realistic ways over these conversations. To model these changes, we gathered a longitudinal video corpus of humanhuman counseling conversations, and constructed a model of changes in articulation rates over multiple conversations. Articulation rates are observed to increase over time, both within a single conversation and across conversations. However, articulation rates increased mainly for words spoken separately from larger phrases. We also present a preliminary evaluation study, showing that implementing such changes in a virtual agent has a measurable effect on user attitudes toward the agent.