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

Rimita Lahiri; Md Nasir; Catherine Lord; So Hyun Kim; Shrikanth Narayanan

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arXiv · 2211.03279

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

Rimita Lahiri·Md Nasir·Catherine Lord·So Hyun Kim·Shrikanth Narayanan

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Abstract

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.

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Rimita Lahiri, Md Nasir, Catherine Lord, So Hyun Kim, Shrikanth Narayanan. 2022-11-07. A Context-Aware Computational Approach for Measuring Vocal Entrainment in Dyadic Conversations. https://arxiv.org/abs/2211.03279

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