TY - GEN
T1 - Conversational Turn-taking as a Stochastic Process on Networks
AU - O'Bryan, Lisa
AU - Segarra, Santiago
AU - Paoletti, Jensine
AU - Zajac, Stephanie
AU - Beier, Margaret E.
AU - Sabharwal, Ashutosh
AU - Wettergreen, Matthew
AU - Salas, Eduardo
N1 - Funding Information:
1Department of Psychological Sciences, Rice University, Houston, TX, USA. 2Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA. 3Department of Bioengineering, Rice University, Houston, TX, USA. Subsets of the team data used in this manuscript have been previously published as part of dissertations by Stephanie Zajac, Department of Psychological Sciences, Rice University and Jian Cao, Department of Electrical and Computer Engineering, Rice University. Funding for this project was provided by a Microsoft Productivity Research Grant, the National Science Foundation (Award Number: 1910117), and the Army Research Institute (Grant Number: W911NF-22-1-0226).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85150221413&partnerID=8YFLogxK
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U2 - 10.1109/IEEECONF56349.2022.10051922
DO - 10.1109/IEEECONF56349.2022.10051922
M3 - Conference contribution
AN - SCOPUS:85150221413
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1243
EP - 1247
BT - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Y2 - 31 October 2022 through 2 November 2022
ER -