Understanding people’s behavior in social interactions is a very interesting problem in Social Computing. In this work, we automatically predict the communication skill of a person in various kinds of social interactions. We consider in particular, 1) Interview-based interactions - asynchronous interviews (web-based interview) Vs. synchronous interviews (regular face-to-face interviews) and 2) Non-interview based interactions - dyad and triad conversations (group discussions). We automatically extract multimodal cues related to verbal and non-verbal behavior content of the interaction. First, in interview-based interactions, we consider previously uninvestigated scenarios of comparing the participant’s behavioral and perceptual changes in the two contexts. Second, we address different manifestations of communication skill in different settings (face-to-face interaction vs. group). Third, the non-interview based interactions also leads to answer research questions such as “the relation between a good communicator and other group variables like dominance or leadership” Finally we look at several attributes (manually annotated) and features/feature groups (automatically extracted) that predicts communication skill well in all settings.
['Sowmya Rasipuram']
Prediction/Assessment of communication skill using multimodal cues in social interactions