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arXiv · 2505.13688

Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations

Seongsil Heo·Calvin Murdock·Michael Proulx·Christi Miller
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Abstract

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.

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Seongsil Heo, Calvin Murdock, Michael Proulx, Christi Miller. 2025-05-19. Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations. https://arxiv.org/abs/2505.13688

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