Automatic Recognition of Affective Laughter in Spontaneous Dyadic Interactions from Audiovisual Signals

Abstract : Laughter is a highly spontaneous behavior that frequently occurs during social interactions. It serves as an expressive-communicative social signal which conveys a large spectrum of affect display. Even though many studies have been performed on the automatic recognition of laughter-or emotion-from audiovisual signals, very little is known about the automatic recognition of emotion conveyed by laughter. In this contribution, we provide insights on emotional laughter by extensive evaluations carried out on a corpus of dyadic spontaneous interactions, annotated with dimensional labels of emotion (arousal and valence). We evaluate, by automatic recognition experiments and correlation based analysis, how different categories of laughter, such as unvoiced laughter, voiced laughter, speech laughter, and speech (non-laughter) can be differentiated from audiovisual features, and to which extent they might convey different emotions. Results show that voiced laughter performed best in the automatic recognition of arousal and valence for both audio and visual features. The context of production is further analysed and results show that, acted and spontaneous expressions of laughter produced by a same person can be differentiated from audiovisual signals, and multilingual induced expressions can be differentiated from those produced during interactions.
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Reshmashree Kantharaju, Fabien Ringeval, Laurent Besacier. Automatic Recognition of Affective Laughter in Spontaneous Dyadic Interactions from Audiovisual Signals. International Conference on Multimodal Interaction (ICMI 2018), Oct 2018, Boulder, CO, United States. pp.220-228, ⟨10.1145/3242969.3243012⟩. ⟨hal-01994000⟩



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