The aim of the Multimodal and Multiperson Corpus of Laughter in Interaction (MMLI) was to collect multimodal data of laughter with the focus on full body movements and different laughter types. It contains both induced and interactive laughs from human triads. In total we collected 500 laugh episodes of 16 participants. The data consists of 3D body position information, facial tracking, multiple audio and video channels as well as physiological data. In this paper we discuss methodological and technical issues related to this data collection including techniques for laughter elicitation and synchronization between different independent sources of data. We also present the enhanced visualization and segmentation tool used to segment captured data. Finally we present data annotation as well as preliminary results of the analysis of the nonverbal behavior patterns in laughter.

MMLI: multimodal multiperson corpus of laughter in interaction / Niewiadomski, R.; Mancini, M.; Baur, T.; Varni, G.; Griffin, H.; Aung, M. S. H.. - STAMPA. - (2013), pp. 184-195. (Intervento presentato al convegno Proceedings of Fourth International Workshop on Human Behavior Understanding (HBU 2013), in conjunction with ACM Multimedia'2013 tenutosi a Barcelona nel 2013) [10.1007/978-3-319-02714-2_16].

MMLI: multimodal multiperson corpus of laughter in interaction

R. Niewiadomski;G. Varni;
2013-01-01

Abstract

The aim of the Multimodal and Multiperson Corpus of Laughter in Interaction (MMLI) was to collect multimodal data of laughter with the focus on full body movements and different laughter types. It contains both induced and interactive laughs from human triads. In total we collected 500 laugh episodes of 16 participants. The data consists of 3D body position information, facial tracking, multiple audio and video channels as well as physiological data. In this paper we discuss methodological and technical issues related to this data collection including techniques for laughter elicitation and synchronization between different independent sources of data. We also present the enhanced visualization and segmentation tool used to segment captured data. Finally we present data annotation as well as preliminary results of the analysis of the nonverbal behavior patterns in laughter.
2013
Lecture Notes in Computer Science 8212
Springer
Springer
Niewiadomski, R.; Mancini, M.; Baur, T.; Varni, G.; Griffin, H.; Aung, M. S. H.
MMLI: multimodal multiperson corpus of laughter in interaction / Niewiadomski, R.; Mancini, M.; Baur, T.; Varni, G.; Griffin, H.; Aung, M. S. H.. - STAMPA. - (2013), pp. 184-195. (Intervento presentato al convegno Proceedings of Fourth International Workshop on Human Behavior Understanding (HBU 2013), in conjunction with ACM Multimedia'2013 tenutosi a Barcelona nel 2013) [10.1007/978-3-319-02714-2_16].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/280603
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