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Gestures augment speech by performing a variety of communicative functions in humans and virtual agents, and are often related to speech by complex semantic, rhetorical, prosodic, and affective elements. In this paper we briefly present an architecture for human-like gesturing in virtual agents that is designed to realize complex speech-to-gesture mappings by exploiting existing machine-learning based parsing tools and techniques to extract these functional elements from speech. We then deeply explore the rhetorical branch of this architecture, objectively assessing specifically whether existing rhetorical parsing techniques can classify gestures into classes with distinct movement properties. To do this, we take a corpus of spontaneously generated gestures and correlate their movement to co-speech utterances. We cluster gestures based on their rhetorical properties, and then by their movement. Our objective analysis suggests that some rhetorical structures are identifiable by our movement features while others require further exploration. We explore possibilities behind these findings and propose future experiments that may further reveal nuances of the richness of the mapping between speech and motion. This work builds towards a real-time gesture generator which performs gestures that effectively convey rich communicative functions.

CMCF: An Architecture for Realtime Gesture Generation by Clustering Gestures by Motion and Communicative Function