Conversational Turn-taking as a Stochastic Process on Networks
Abstract
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.
Cite
@article{arxiv.2301.04030,
title = {Conversational Turn-taking as a Stochastic Process on Networks},
author = {Lisa O'Bryan and Santiago Segarra and Jensine Paoletti and Stephanie Zajac and Margaret E. Beier and Ashutosh Sabharwal and Matthew Wettergreen and Eduardo Salas},
journal= {arXiv preprint arXiv:2301.04030},
year = {2023}
}
Comments
5 pages, 2 figures. To be published in the 2022 Conference Proceedings of the Asilomar Conference on Signals, Systems and Computers