Laughter Detection in Meetings

Abstract

We build a system to automatically detect laughter events in meetings, where laughter events are defined as points in the meeting where a number of the participants (more than just one) are laughing simultaneously. We implement our system using a support vector machine classifier trained on mel-frequency cepstral coefficients (MFCCs), delta MFCCs, modulation spectrum, and spatial cues from the time delay between two desktop microphones. We run our experiments on the 'Bmr' subset of the ICSI Meeting Recorder corpus using just two table-top microphones and obtain detection results with a correct accept rate of 87% and a false alarm rate of 13%.