  Interactional synchrony refers to how the speech or behavior of two or more
people involved in a conversation become more finely synchronized with each
other, and they can appear to behave almost in direct response to one another.
Studies have shown that interactional synchrony is a hallmark of relationships,
and is produced as a result of rapport. %Research has also shown that up to
two-thirds of human communication occurs via nonverbal channels such as
gestures (or body movements), facial expressions, \etc.
  In this work, we use computer vision based methods to extract nonverbal cues,
specifically from the face, and develop a model to measure interactional
synchrony based on those cues. This paper illustrates a novel method of
constructing a dynamic deep neural architecture, specifically made up of
intermediary long short-term memory networks (LSTMs), useful for learning and
predicting the extent of synchrony between two or more processes, by emulating
the nonlinear dependencies between them. On a synthetic dataset, where pairs of
sequences were generated from a Gaussian process with known covariates, the
architecture could successfully determine the covariance values of the
generating process within an error of 0.5% when tested on 100 pairs of
interacting signals. On a real-life dataset involving groups of three people,
the model successfully estimated the extent of synchrony of each group on a
scale of 1 to 5, with an overall prediction mean of $2.96%$ error when
performing 5-fold validation, as compared to 26.1% on the random permutations
serving as the control baseline.
