Predicting next speaker based on head movement in multi-party meetings

We proposed a model for predicting the next speaker in multi-party meetings by focusing on the participants´ head movements measured by using a six degrees-of-freedom head tracker. Results of an analysis of head movements collected from multi-party meetings revealed differences in the amounts, amplitude, and frequency of movement of the head position and rotation of the speaker near the end of an utterance in turn-keeping and turn-taking. The results also revealed the differences in the amounts of movement, amplitude, and frequency of head position movement and rotation between the listeners in turn-keeping, turn-taking, and the next speaker in turn-taking. We then built a next speaker prediction model that features two processing steps to predict whether turn-taking or turn-keeping will occur and who the next speaker will be in turn-taking. The evaluation results for the model suggest that the speaker´s and listeners´ head movements contribute to predicting the next speaker.