Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
A new discriminative kernel from probabilistic models
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Component-based discriminative classification for hidden Markov models
Pattern Recognition
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Modeling dominance in group conversations using nonverbal activity cues
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
Guest editorial: special issue on human computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Modeling individual and group actions in meetings with layered HMMs
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Analysis and classification of conversational interactions
Proceedings of the international conference on Multimedia
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The analysis of human conversations under a social signalling perspective recently raised the joint attention of pattern recognition and psychology researchers. In particular, the dialog classification represents an appealing recent application whose aim is to go beyond the meaning of the spoken words, focusing instead on the way the sentences are pronounced by capturing natural (or hidden) characteristics, such the mood of the conversation. An effective strategy to face this issue is to encode the turn-taking dynamics in a generative model, whose structure is composed by conditional dependencies among first-order Markov processes. In this paper, we follow this strategy, investigating how to boost the classification performances of this model and of the related higherorder Markov extensions, through the definition of a novel generative score space. Generative score spaces are employed to increase generative classification in a discriminative way, also allowing a deep understanding of the processed data through the use of standard pattern recognition strategies. Experiments on real data certify the goodness of our intuition.