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

arXiv: https://arxiv.org/abs/2211.03279 · HTML: https://mlsanity.com/paper/2211.03279/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder](https://mlsanity.com/paper/2409.13606/) (2024)
- [Acoustic and Facial Markers of Perceived Conversational Success in Spontaneous Speech](https://mlsanity.com/paper/2604.15322/) (2026)
- [Articulatory Entrainment and Coordination Complexity in Spontaneous Autistic and Non-autistic Dialogue](https://mlsanity.com/paper/2610.04071/) (2026)

## Abstract (arXiv)

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.