Acoustic-Prosodic Entrainment in Multi-party Spoken Dialogues: Does Simple Averaging Extend Existing Pair Measures Properly?

Linguistic entrainment, the tendency of interlocutors to become similar to each other during spoken interaction, is an important characteristic of human speech. Implementing linguistic entrainment in spoken dialogue systems helps to improve the naturalness of the conversation, likability of the agents, and dialogue and task success. The first step toward implementation of such systems is to design proper measures to quantify entrainment. Multi-party entrainment and multi-party spoken dialogue systems have received less attention compared to dyads. In this study, we analyze an existing approach of extending pair measures to team-level entrainment measures, which is based on simple averaging of pairs. We argue that although simple averaging is a good starting point to measure team entrainment, it has several weaknesses in terms of capturing team-specific behaviors specifically related to convergence.