Analysis of the occurrence of laughter in meetings

Automatic speech understanding in natural multiparty conversation settings stands to gain from parsing not only verbal but also non-verbal vocal communicative behaviors. In this work, we study the most frequently annotated non-verbal behavior, laughter, whose detection has clear implications for speech understanding tasks, and for the automatic recognition of affect in particular. To complement existing acoustic descriptions of the phenomenon, we explore the temporal patterning of laughter over the course of conversation, with a view towards its automatic segmentation and detection. We demonstrate that participants vary extensively in their use of laughter, and that laughter differs from speech in its duration and in the regularity of its occurrence. We also show that laughter and speech are quite dissimilar in terms of the degree of simultaneous vocalization by multiple participants, and in terms of the probability of transitioning into and out of vocalization overlap states. Index Terms: laughter, vocal interaction, multiparty meetings, Markov models.