Machine Learning
Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Gaze and Gesture Activity in Communication
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
ACM Transactions on Applied Perception (TAP)
Eye-gaze experiments for conversation monitoring
Proceedings of the 3rd International Universal Communication Symposium
Reasoning for video-mediated group communication
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Enabling Composition-Based Video-Conferencing for the Home
IEEE Transactions on Multimedia
Hard lessons learned: mobile eye-tracking in cockpits
Proceedings of the 4th Workshop on Eye Gaze in Intelligent Human Machine Interaction
Proceedings of the 4th Workshop on Eye Gaze in Intelligent Human Machine Interaction
Computational approaches to visual attention for interaction inference
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Inferential methods in interaction, usability and user experience
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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This paper discusses estimation of active speaker in multi-party video-mediated communication from gaze data of one of the participants. In the explored settings, we predict voice activity of participants in one room based on gaze recordings of a single participant in another room. The two rooms were connected by high definition, low delay audio and video links and the participants engaged in different activities ranging from casual discussion to simple problem-solving games. We treat the task as a classification problem. We evaluate several types of features and parameter settings in the context of Support Vector Machine classification framework. The results show that using the proposed approach vocal activity of a speaker can be correctly predicted in 89 % of the time for which the gaze data are available.