# Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations

arXiv: https://arxiv.org/abs/2505.13688 · HTML: https://mlsanity.com/paper/2505.13688/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2025.

## Built on by (most cited first, 2 of 2)

- [MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild](https://mlsanity.com/paper/2606.16731/) (2026)
- [Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings](https://mlsanity.com/paper/2606.17542/) (2026)

## Abstract (arXiv)

Turn-taking prediction is crucial for seamless interactions. This study introduces a novel, lightweight framework for accurate turn-taking prediction in triadic conversations without relying on computationally intensive methods. Unlike prior approaches that either disregard gaze or treat it as a passive signal, our model integrates gaze with speaker localization, structuring it within a spatial constraint to transform it into a reliable predictive cue. Leveraging egocentric behavioral cues, our experiments demonstrate that incorporating gaze data from a single-user significantly improves prediction performance, while gaze data from multiple-users further enhances it by capturing richer conversational dynamics. This study presents a lightweight and privacy-conscious approach to support adaptive, directional sound control, enhancing speech intelligibility in noisy environments, particularly for hearing assistance in smart glasses.