An information-theoretic framework for automated discovery of prosodic cues to conversational structure

Interaction timing in conversation exhibits myriad variabilities, yet it is patently not random. However, identifying consistencies is a manually labor-intensive effort, and findings have been limited. We propose a conditonal mutual information measure of the influence of prosodic features, which can be computed for any conversation at any instant, with only a speech/non-speech segmentation as its requirement. We evaluate the methodology on two segmental features: energy and speaking rate. Results indicate that energy, the less controversial of the two, is in fact better on average at predicting conversational structure. We also explore the temporal evolution of model “surprise”, which permits identifying instants where each feature's influence is operative. The method corroborates earlier findings, and appears capable of large-scale data-driven discovery in future research.