A generative score space for statistical dialog characterization in social signalling

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 higher-order 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.