Social roles characterize relation between participants in a conversation and, in turn, influence their interaction patterns. This paper investigates automatic social role recognition in professional meetings using a completely discriminative framework based on conditional random fields. We present a novel approach which combines information from multiple layers of data. The conversation layer models the influence of social roles on turn taking patterns of participants present in multiparty interactions. A conditional random field augmented with hidden state sequences is used to estimate the posterior distribution of social roles in this layer. The other novelty of our approach consists in modeling statistical dependencies between roles across adjacent segments of meeting. The posterior distribution estimated in conversation layer is combined with role transition information to improve the model. Experiments conducted on more than 40 hours of data reveal that the proposed approach reaches a recognition accuracy of 67% in classifying four social roles using information from conversation layer. Moreover, recognition accuracy increases to 70% when information from multiple layers is taken into consideration.
Bibliographic reference. Sapru, Ashtosh / Bourlard, Hervé (2013): "Automatic social role recognition in professional meetings using conditional random fields", In INTERSPEECH-2013, 1530-1534.