Decision-level fusion for audio-visual laughter detection
conference paper
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. © 2008 Springer-Verlag Berlin Heidelberg.
Topics
TNO Identifier
241311
ISSN
03029743
ISBN
3540858520 ; 9783540858522
Source title
5th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2008, 8 September 2008 through 10 September 2008, Utrecht
Pages
137-148
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