  Understanding why certain individuals work well (or poorly) together as a
team is a key research focus in the psychological and behavioral sciences and a
fundamental problem for team-based organizations. Nevertheless, we have a
limited ability to predict the social and work-related dynamics that will
emerge from a given combination of team members. In this work, we model vocal
turn-taking behavior within conversations as a parametric stochastic process on
a network composed of the team members. More precisely, we model the dynamic of
exchanging the `speaker token' among team members as a random walk in a graph
that is driven by both individual level features and the conversation history.
We fit our model to conversational turn-taking data extracted from audio
recordings of multinational student teams during undergraduate engineering
design internships. Through this real-world data we validate the explanatory
power of our model and we unveil statistically significant differences in
speaking behaviors between team members of different nationalities.
