In this paper, we focus on inferring social roles in conversations using information extracted only from the speaking styles of the speakers. We use dynamic Bayesian networks (DBNs) to model the turn-taking behavior of the speakers. DBNs provide the capability of naturally formulating the dependencies between random variables. Specifically, we first model our problem as a hidden Markov model (HMM). As it turns out, the knowledge of the segments that belong to the same speaker can be augmented into this HMMstructure to form a DBN. This information places a constraint on two subsequent speaker roles such that the current speaker role depends not only on the previous speakerís 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 the constraint on same speaker segments assigned 89.9% turns the correct role whereas the HMM-based approach assigned 79.2% of turns their correct role.
Bibliographic reference. Yaman, Sibel / Hakkani-Tür, Dilek / Tur, Gokhan (2010): "Social role discovery from spoken language using dynamic Bayesian networks", In INTERSPEECH-2010, 2870-2873.