A statistical signature for automatic dialogue classification

In the last few years, there has been a certain attention 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 designed an automatic system based on a generative structure able to classify hard dialog acts. The generative model is composed by integrating a hierarchical Gaussian mixture model and the Influence Model, originating a brand new method able to deal with such difficult scenarios. The method has been tested on a set of conversational settings involving dialogues between adults and children and adults, in flat and arguing discussions, proving very accurate classification results.