  Vocal entrainment is a social adaptation mechanism in human interaction,
knowledge of which can offer useful insights to an individual's
cognitive-behavioral characteristics. We propose a context-aware approach for
measuring vocal entrainment in dyadic conversations. We use conformers(a
combination of convolutional network and transformer) for capturing both
short-term and long-term conversational context to model entrainment patterns
in interactions across different domains. Specifically we use cross-subject
attention layers to learn intra- as well as inter-personal signals from dyadic
conversations. We first validate the proposed method based on classification
experiments to distinguish between real(consistent) and
fake(inconsistent/shuffled) conversations. Experimental results on interactions
involving individuals with Autism Spectrum Disorder also show evidence of a
statistically-significant association between the introduced entrainment
measure and clinical scores relevant to symptoms, including across gender and
age groups.
