Abstract
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.
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Laskowski, K., Schultz, T. (2008). Detection of Laughter-in-Interaction in Multichannel Close-Talk Microphone Recordings of Meetings. In: Popescu-Belis, A., Stiefelhagen, R. (eds) Machine Learning for Multimodal Interaction. MLMI 2008. Lecture Notes in Computer Science, vol 5237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85853-9_14
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DOI: https://doi.org/10.1007/978-3-540-85853-9_14
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