Automated Laughter Detection From Full-Body Movements

Abstract—In this paper, we investigate the detection of laughter from the user’s nonverbal full-body movement in social and ecological contexts. Eight hundred and one laughter and nonlaughter segments of full-body movement were examined from a corpus of motion capture data of subjects participating in social activities that stimulated laughter. A set of 13 full-body movement features was identified, and corresponding automated extraction algorithms were developed. These features were extracted from the laughter and nonlaughter segments, and the resulting dataset was provided as input to supervised machine learning techniques. Both discriminative (radial basis function-support vector machines, k-nearest neighbor, and random forest) and probabilistic (naive Bayes and logistic regression) classifiers were trained and evaluated. A comparison of automated classification with the ratings of human observers for the same laughter and nonlaughter segments showed that the performance of o...

• Type: Journal
• Year: 2016
• Where: THMS
• Authors: Radoslaw Niewiadomski, Maurizio Mancini, Giovanna Varni, Gualtiero Volpe, Antonio Camurri
• Keywords: THMS 2016