Work

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

Year: 2023

Type: conference-paper

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 association between the introduced entrainment measure and clinical scores relevant to symptoms, including across gender and age groups.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp49357.2023.10095512

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

Institutions: University of Southern California, Microsoft (United States), University of California, Los Angeles, Korea University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4372260019

FWCI: 1.843

Cites: 20

Cited by: 5

Related to: 10