Automatic Detection of Laughter and Fillers in Spontaneous Mobile Phone Conversations

This article presents experiments on automatic detection of laughter and fillers, two of the most important nonverbal behavioral cues observed in spoken conversations. The proposed approach is fully automatic and segments audio recordings captured with mobile phones into four types of interval: laughter, filler, speech and silence. The segmentation methods rely not only on probabilistic sequential models (in particular Hidden Markov Models), but also on Statistical Language Models aimed at estimating the a-priori probability of observing a given sequence of the four classes above. The experiments are speaker independent and performed over a total of 8 hours and 25 minutes of data (120 people in total). The results show that F₁ scores up to 0.64 for laughter and 0.58 for fillers can be achieved.