Automatic annotation of gestural units in spontaneous face-to-face interaction

Abstract

Speech and gesture co-occur in spontaneous dialogue in a highly complex fashion. There is a large variability in the motion that people exhibit during a dialogue, and different kinds of motion occur during different states of the interaction. A wide range of multimodal interface applications, for example in the fields of virtual agents or social robots, can be envisioned where it is important to be able to automatically identify gestures that carry information and discriminate them from other types of motion. While it is easy for a human to distinguish and segment manual gestures from a flow of multimodal information, the same task is not trivial to perform for a machine. In this paper we present a method to automatically segment and label gestural units from a stream of 3D motion capture data.