  Laughter is considered one of the most overt signals of joy. Laughter is
well-recognized as a multimodal phenomenon but is most commonly detected by
sensing the sound of laughter. It is unclear how perception and annotation of
laughter differ when annotated from other modalities like video, via the body
movements of laughter. In this paper we take a first step in this direction by
asking if and how well laughter can be annotated when only audio, only video
(containing full body movement information) or audiovisual modalities are
available to annotators. We ask whether annotations of laughter are congruent
across modalities, and compare the effect that labeling modality has on machine
learning model performance. We compare annotations and models for laughter
detection, intensity estimation, and segmentation, three tasks common in
previous studies of laughter. Our analysis of more than 4000 annotations
acquired from 48 annotators revealed evidence for incongruity in the perception
of laughter, and its intensity between modalities. Further analysis of
annotations against consolidated audiovisual reference annotations revealed
that recall was lower on average for video when compared to the audio
condition, but tended to increase with the intensity of the laughter samples.
Our machine learning experiments compared the performance of state-of-the-art
unimodal (audio-based, video-based and acceleration-based) and multi-modal
models for different combinations of input modalities, training label modality,
and testing label modality. Models with video and acceleration inputs had
similar performance regardless of training label modality, suggesting that it
may be entirely appropriate to train models for laughter detection from body
movements using video-acquired labels, despite their lower inter-rater
agreement.
