Decision-Level Fusion for Audio-Visual Laughter Detection

Authors:
Boris Reuderink;Mannes Poel;Khiet Truong;Ronald Poppe;Maja Pantic
Affiliations:
University of Twente, Enschede, The Netherlands 7500 AE;University of Twente, Enschede, The Netherlands 7500 AE;University of Twente, Enschede, The Netherlands 7500 AE and TNO Defence, Sec. and Safety, , Soesterberg, The Netherlands 3769 ZG;University of Twente, Enschede, The Netherlands 7500 AE;University of Twente, Enschede, The Netherlands 7500 AE and Imperial College Dept. of Computing, , London, UK SW7 2AZ
Venue:
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Year:
2008

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Abstract

Laughter is a highly variable signal, which can be caused by a spectrum of emotions. This makes the automatic detection of laughter a challenging, but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by fusing the results of separate audio and video classifiers on the decision level. This results in laughter detection with a significantly higher AUC-ROC than single-modality classification.