Prediction of audience response from spoken sequences, speech pauses and co-speech gestures in humorous discourse by Barack Obama

Abstract

In this paper, we aim to predict audience response from simple spoken sequences, speech pauses and co-speech gestures in annotated video-and audio-recorded speeches by Barack Obama at the Annual White House Correspondents' Association Dinner in 2011 and 2016. At these dinners, the American president mocks himself, his collaborators, political adversary and the press corps making the audience react with cheers, laughter and/or applause. The results of the prediction experiment demonstrate that information about spoken sequences, pauses and co-speech gestures by Obama can be used to predict the immediate audience response. This confirms and shows an application of numerous studies that address the importance of speech pauses and gestures in delivering the discourse message in a successful way. The fact that machine learning algorithms can use information about pauses and gestures to build models of audience reaction is also relevant for the construction of intelligent and cognitively based multimodal ICT.