Predicting and Regulating Participation Equality in Human-robot Conversations

Abstract

In this paper, we investigate participation equality, in terms of speaking time, between users in multi-party human-robot conversations. We analyse a dataset where pairs of users (540 in total) interact with a conversational robot exhibited at a technical museum. The data encompass a wide range of different users in terms of age (adults/children) and gender (male/female), in different combinations. Overall, the analysis indicates that demographically heterogeneous pairs are more imbalanced, especially pairs of adults and children, where children are less prone to self-select in the turn-taking. The analysis also indicates that it is possible for the robot to reduce the imbalance by addressing the least dominant user and asking directed questions. However, for children to respond, it is important to seek mutual gaze and switch addressee often. Finally, we show that it is possible to predict the imbalance at an early stage in the interaction -- in order to increase the participation equality as early as possible -- and that knowledge about the users' age and gender helps in this prediction.