Generative Modeling and Classification of Dialogs by a Low-level Turn-taking Feature

In the last few years, a growing attention has been paid to the problem of human-human communication, trying to devise artificial systems able to mediate a conversational setting between two or more people. In this paper, we propose an automatic system based on a generative structure able to classify dialog scenarios. The generative model is composed by integrating a Gaussian mixture model and an (observed) markovian influence model, and it is fed with a novel low-level acoustic feature termed steady conversational period (SCP). SCPs 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, and may be important for predicting the evolution of typical conversational situations in di®erent dialog scenarios. The model has been tested on an extensive set of real conversational settings involving dialogs between adults and between children and adults, in flat and arguing discussions, proving to achieve very accurate classification results.