Multilingual spoken-language understanding in the MIT Voyager system
Speech Communication
A maximum entropy approach to natural language processing
Computational Linguistics
Multimodal integration: a biological view
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Poster abstract: Gesture recognition via continuous maximum entropy training on accelerometer data
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
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Multimodal Integration addresses the problem of combining various user inputs into a single semantic representation that can be used in deciding the next step of system action(s). The method presented in this paper uses a statistical framework to implement the integration mechanism and includes contextual information additionally to the actual user input. The underlying assumption is that the more information sources are taken into account, the better picture can be drawn about the actual intention of the user in the given context of the interaction. The paper presents the latest results with a Maximum Entropy classifier, with special emphasis on the use of contextual information (type of gesture movements and type of objects selected). Instead of explaining the design and implementation process in details (a longer paper to be published later will do that), only a short description is provided here about the demonstration implementation that produces above 91% accuracy for the 1st best and higher than 96% for the accumulated five N-bests results.