Generative multimodal models of nonverbal synchrony in close relationships

Positive interpersonal relationships require shared understanding along with a sense of rapport. A key facet of rapport is mirroring and convergence of facial expression and body language, known as nonverbal synchrony. We examined nonverbal synchrony in a study of 29 heterosexual romantic couples, in which audio, video, and bracelet accelerometer were recorded during three conversations. We extracted facial expression, body movement, and acoustic-prosodic features to train neural network models that predicted the nonverbal behaviors of one partner from those of the other. Recurrent models (LSTMs) outperformed feed-forward neural networks and other chance baselines. The models learned behaviors encompassing facial responses, speech-related facial movements, and head movement. However, they did not capture fleeting or periodic behaviors, such as nodding, head turning, and hand gestures. Notably, a preliminary analysis of clinical measures showed greater association with our model outputs than correlation of raw signals. We discuss potential uses of these generative models as a research tool to complement current analytical methods along with real-world applications (e.g., as a tool in therapy).