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Vocal entrainment is a social adaptation mechanism in human interaction,knowledge of which can offer useful insights to an individual'scognitive-behavioral characteristics. We propose a context-aware approach formeasuring vocal entrainment in dyadic conversations. We use conformers(acombination of convolutional network and transformer) for capturing bothshort-term and long-term conversational context to model entrainment patternsin interactions across different domains. Specifically we use cross-subjectattention layers to learn intra- as well as inter-personal signals from dyadicconversations. We first validate the proposed method based on classificationexperiments to distinguish between real(consistent) andfake(inconsistent/shuffled) conversations. Experimental results on interactionsinvolving individuals with Autism Spectrum Disorder also show evidence of astatistically-significant association between the introduced entrainmentmeasure and clinical scores relevant to symptoms, including across gender andage groups.

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