Laughter detection based on the fusion of local binary patterns, spectral and prosodic features

Today, great focus has been placed on context-aware human-machine interaction, where systems are aware not only of the surrounding environment, but also about the mental/affective state of the user. Such knowledge can allow for the interaction to become more human-like. To this end, automatic discrimination between laughter and speech has emerged as an interesting, yet challenging problem. Typically, audio-or video-based methods have been proposed in the literature; humans, however, are known to integrate both sensory modalities during conversation and/or interaction. As such, this paper explores the fusion of support vector machine classifiers trained on local binary pattern (LBP) video features, as well as speech spectral and prosodic features as a way of improving laughter detection performance. Experimental results on the publicly-available MAHNOB Laughter database show that the proposed audio-visual fusion scheme can achieve a laughter detection accuracy of 93.3%, thus outperforming systems trained on audio or visual features alone.