Temporal Association Rules for Modelling Multimodal Social Signals

In this paper, we present the first step of a methodology dedicated to deduce automatically sequences of signals expressed by humans during an interaction. The aim is to link interpersonal stances with arrangements of social signals such as modulations of Action Units and prosody during a face-to-face exchange. The long-term goal is to infer association rules of signals. We plan to use them as an input to the animation of an Embodied Conversational Agent (ECA). In this paper, we illustrate the proposed methodology to the SEMAINE-DB corpus from which we automatically extracted Action Units (AUs), head positions, turn-taking and prosody information. We have applied the data mining algorithm that is used to find the sequences of social signals featuring different social stances. We finally discuss our primary results focusing on given AUs (smiles and eyebrows) and the perspectives of this method.