Social Role Discovery from Spoken Language using Dynamic Bayesian Networks

In this paper, we focus on inferring social roles in conversations using information extracted only from the speaking styles of the speakers. We model the turn-taking behavior of the speakers with dynamic Bayesian networks (DBNs), which provide the capability of naturally formulating the dependencies between random variables. More specifically, we first explore the usefulness of a simple DBN, namely, a hidden Markov model (HMM), for this problem. As it turns out, the knowledge of the segments that belong to the same speaker can be augmented into this HMM structure, which results in a more sophisticated DBN. This information places a constraint on two subsequent speaker roles such that the current speaker role depends not only on the previous speaker’s role but also on that most recent role assigned to the same speaker. We conducted an experimental study to compare these two modeling approaches using broadcast shows. In our experiments, the approach with Annual Conference of the International Speech Communication Association (Interspeech) the constraint on same speaker segments assigned 89.5% turns the correct role whereas the HMM-based approach assigned 79.2% of turns their correct role.