Prediction of Next-Utterance Timing using Head Movement in Multi-Party Meetings

To build a conversational interface wherein an agent system can smoothly communicate with multiple persons, it is imperative to know how the timing of speaking is decided. In this research, we explore the head movements of participants as an easy-to-measure nonverbal behavior to predict the nest-utterance timing, i.e., the interval between the end of the current speaker's utterance and the start of the next speaker's utterance, in turn-changing in multi-party meetings. First, we collected data on participants' six degree-of-freedom head movements and utterances in four-person meetings. The results of the analysis revealed that the amount of head movements of current speaker, next speaker, and listeners have a positive correlation with the utterance interval. Moreover, the degree of synchrony of the head position and posture between the current speaker and next speaker is negatively correlated with the utterance interval. On the basis of these findings, we used their head movements and the synchrony of their head movements as feature values and devised several prediction models. A model using all features performed the best and was able to predict the next-utterance timing well. Therefore, this research revealed that the participants' head movement is useful for predicting the next-utterance timing in turn-changing in multi-party meetings.