Audience engagement prediction in guided tours through multimodal features

This paper explores the possibility to predict audience engagement, measured in terms of visible attention, in the context of guided tours. We built a dataset composed of Italian sentences derived from the speech of an expert guide leading visitors in cultural sites, enriched with multimodal features, and labelled on the basis of the perceivable engagement of the audience. We run experiments in various classification scenarios and observed the impact of modality-specific features on the classifiers.