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.
@article{arxiv.2211.03279,
title = {A Context-Aware Computational Approach for Measuring Vocal Entrainment in Dyadic Conversations},
author = {Rimita Lahiri and Md Nasir and Catherine Lord and So Hyun Kim and Shrikanth Narayanan},
journal= {arXiv preprint arXiv:2211.03279},
year = {2022}
}