Auditory dialog analysis and understanding by generative modelling of interactional dynamics

In the last few years, the interest in the analysis of human behavioral schemes has dramatically grown, in particular for the interpretation of the communication modalities called social signals. They represent well defined interaction patterns, possibly unconscious, characterizing different conversational situations and behaviors in general. In this paper, we illustrate an automatic system based on a generative structure able to analyze conversational scenarios. The generative model is composed by integrating a Gaussian mixture model and the (observed) influence model, and it is fed with a novel kind of simple low-level auditory social signals, which are termed steady conversational periods (SCPs). These are built on duration of continuous slots of silence or speech, taking also into account conversational turn-taking. The interactional dynamics built upon the transitions among SCPs provide a behavioral blueprint of conversational settings without relying on segmental or continuous phonetic features. Our contribution here is to show the effectiveness of our model when applied on dialogs classification and clustering tasks, considering dialogs between adults and between children and adults, in both flat and arguing discussions, and showing excellent performances also in comparison with state-of-the-art frameworks