A Context-Aware Computational Approach for Measuring Vocal Entrainment in Dyadic Conversations

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 interpersonal 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 (ASD) 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.