Fusion of audio and visual cues for laughter detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Affective feedback: an investigation into the role of emotions in the information seeking process
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Audiovisual laughter detection based on temporal features
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Affective ranking of movie scenes using physiological signals and content analysis
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle filtering with factorized likelihoods for tracking facial features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Toward Pose-Invariant 2-D Face Recognition Through Point Distribution Models and Facial Symmetry
IEEE Transactions on Information Forensics and Security - Part 1
A phonetic analysis of natural laughter, for use in automatic laughter processing systems
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments
Image and Vision Computing
Human behavior sensing for tag relevance assessment
Proceedings of the 21st ACM international conference on Multimedia
| Hi-index | 0.00 |
This paper presents the results of an empirical study suggesting that, while laughter is a very good indicator of amusement, the kind of laughter (unvoiced laughter vs.voiced laugh ter) is correlated with the mirth of laughter and could potential be used to judge the actual hilarity of the stimulus joke. For this study, an automated method for audiovisual analysis of laugher episodes exhibited while watching movie clips or observing the behaviour of a conversational agent has been developed. The audio and visual features, based on spectral properties of the acoustic signal and facial expressions respectively, have been integrated using feature level fusion, resulting in a multimodal approach to distinguishing voiced laughter from unvoiced laughter and speech. The classification accuracy of such a system tested on spontaneous laughter episodes is 74 %. Finally, preliminary results are presented which provide evidence that unvoiced laughter can be interpreted as less gleeful than voiced laughter and consequently the detection of those two types of laughter can be used to label multimedia content as little funny or very funny respectively.