Abstract
Social Signal Processing techniques have given the opportunity to analyze in-depth human behavior in social face-to-face interactions. With recent advancements, it is henceforth possible to use these techniques to augment social interactions, especially the human behavior in oral presentations. The goal of this paper is to train a computational model able to provide a relevant feedback to a public speaker concerning his coverbal communication. Hence, the role of this model is to augment the social intelligence of the orator and then the relevance of his presentation. To this end, we present an original interaction setting in which the speaker is equipped with only wearable devices. Several coverbal modalities have been extracted and automatically annotated namely speech volume, intonation, speech rate, eye gaze, hand gestures and body movements. An offline report was addressed to participants containing the performance scores on the overall modalities. In addition, a post-experiment study was conducted to collect participant's opinions on many aspects of the studied interaction and the results were rather positive. Moreover, we annotated recommended feedbacks for each presentation session, and to retrieve these annotations, a Dynamic Bayesian Network model was trained using as inputs the multimodal performance scores. We will show that our assessment behavior model presents good performances compared to other models.