Is Turn-Shift Distinguishable with Synchrony?

During an interaction, interlocutors emit multimodal social signals to communicate their intent by exchanging speaking turns smoothly or through interruptions, and adapting to their interacting partners which is referred to as interpersonal synchrony. We are interested in understanding whether the synchrony of multimodal signals could help to distinguish different types of turn-shifts. We consider three types of turn-shifts: smooth turn exchange, interruption and backchannel in this paper. We segmented each turn-shift into three phases: before, during and after, we calculated the synchrony measures of the three phases for multimodal signals (facial expression, head pose, and low-level acoustic features). In this paper, a brief analysis of synchronization during turn-shifts is presented, we also study the evolution of interpersonal synchrony before, during and after the turn-shifts. We proposed computational models for the turn-shift classification task only using synchrony measures. The best performance was obtained with an FNN model using the three phases’ synchrony score of all features (accuracy of 0.75).