Coupled Systems for Modeling Rapport between Interlocutors

This research work explores different machine learning techniques for recognizing the existence of rapport between two people engaged in a conversation, based on their facial expressions. First using artificially generated pairs of correlated data signals, a coupled gated recurrent unit (cGRU) neural network is developed to measure the extent of similarity between the temporal evolution of pairs of time-series signals. By pre-selecting their covariance values (between 0.1 and 1.0), pairs of coupled sequences are generated. Using the developed cGRU architecture, this covariance between the signals is successfully recovered. Using this and various other coupled architectures, tests for rapport (measured by the extent of mirroring and mimicking of behaviors) are conducted on real-life datasets. On fifty-nine (N = 59) pairs of interactants in an interview setting, a transformer based coupled architecture performs the best in determining the existence of rapport. To test for generalization, the models were applied on never-been-seen data collected 14 years prior, also to predict the existence of rapport. The coupled transformer model again performed the best for this transfer learning task, determining which pairs of interactants had rapport and which did not. The experiments and results demonstrate the advantages of coupled architectures for predicting an interactional process such as rapport, even in the presence of limited data.