Discourse segmentation of multi-party conversation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Automatic discrimination between laughter and speech
Speech Communication
Speech activity detection on multichannels of meeting recordings
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
The rich transcription 2006 spring meeting recognition evaluation
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
VIRUS: video information retrieval using subtitles
Proceedings of the 14th International Academic MindTrek Conference: Envisioning Future Media Environments
Image and Vision Computing
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Laughter is a key element of human-human interaction, occurring surprisingly frequently in multi-party conversation. In meetings, laughter accounts for almost 10% of vocalization effort by time, and is known to be relevant for topic segmentation and the automatic characterization of affect. We present a system for the detection of laughter, and its attribution to specific participants, which relies on simultaneously decoding the vocal activity of all participants given multi-channel recordings. The proposed framework allows us to disambiguate laughter and speech not only acoustically, but also by constraining the number of simultaneous speakers and the number of simultaneous laughers independently, since participants tend to take turns speaking but laugh together. We present experiments on 57 hours of meeting data, containing almost 11000 unique instances of laughter.