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 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 …

Date

2023

Authors

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

Conference

ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

1-5

Publisher

IEEE