Social and acoustic determinants of perceived laughter intensity

Existing research links subjective judgments of perceived laughter intensity with features such as duration, amplitude, fundamental frequency, and voicing. We examine these associations in a new database of social laughs produced in situations inducing amusement, embarrassment, and schadenfreude. We also test the extent to which listeners’ judgments of laughter intensity vary as a function of the social situation in which laughs were produced

Social and acoustic determinants of perceived laughter intensity

Existing research links subjective judgments of perceived laughter intensity with features such as duration, amplitude, fundamental frequency, and voicing. We examine these associations in a new database of social laughs produced in situations inducing amusement, embarrassment, and schadenfreude. We also test the extent to which listeners’ judgments of laughter intensity vary as a function of the social situation in which laughs were produced

Automatic Discrimination of Laughter Using Distributed sEMG

Laughter is a very interesting non-verbal human vocalization. It is classified as a semi voluntary behavior despite being a direct form of social interaction, and can be elicited by a variety of very different stimuli, both cognitive and physical. Automatic laughter detection, analysis and classification will boost progress in affective computing, leading to the development of more natural human-machine communication interfaces. Surface Electromyography (sEMG) on abdominal muscles or invasive EMG on the larynx show potential in this direction, but these kinds of EMG-based sensing systems cannot be used in ecological settings due to their size, lack of reusability and uncomfortable setup. For this reason, they cannot be easily used for natural detection and measurement of a volatile social behavior like laughter in a variety of different situations. We propose the use of miniaturized, wireless, dry-electrode sEMG sensors on the neck for the detection and analysis of laughter. Even if with this solution the activation of specific larynx muscles cannot be precisely measured, it is possible to detect different EMG patterns related to larynx function. In addition, integrating sEMG analysis on a multisensory compact system positioned on the neck would improve the overall robustness of the whole sensing system, enabling the synchronized measure of different characteristics of laughter, like vocal production, head movement or facial expression; being at the same time less intrusive, as the neck is normally more accessible than abdominal muscles. In this paper, we report laughter discrimination rate obtained with our system depending on different conditions

Laughter Induction Techniques Suitable for Generating Motion Capture Data of Laughter Associated Body Movements

Laughter is a frequently occurring social signal and an important part of human non-verbal communication. However it is often overlooked as a serious topic of scientific study. While the lack of research in this area is mostly due to laughter's non-serious nature, it is also a particularly difficult social signal to produce on demand in a convincing manner; thus making it a difficult topic for study in laboratory settings. In this paper we provide some techniques and guidance for inducing both hilarious laughter and conversational laughter. These techniques were devised with the goal of capturing motion information related to laughter while the person laughing was either standing or seated. Comments on the value of each of the techniques and general guidance as to the importance of atmosphere, environment and social setting are provided. © 2013 IEEE