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arXiv · 2510.19350

Modeling Turn-Taking with Semantically Informed Gestures

Varsha Suresh·M. Hamza Mughal·Christian Theobalt·Vera Demberg
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Abstract

In conversation, humans use multimodal cues, such as speech, gestures, and gaze, to manage turn-taking. While linguistic and acoustic features are informative, gestures provide complementary cues for modeling these transitions. To study this, we introduce DnD Gesture++, an extension of the multi-party DnD Gesture corpus enriched with 2,663 semantic gesture annotations spanning iconic, metaphoric, deictic, and discourse types. Using this dataset, we model turn-taking prediction through a Mixture-of-Experts framework integrating text, audio, and gestures. Experiments show that incorporating semantically guided gestures yields consistent performance gains over baselines, demonstrating their complementary role in multimodal turn-taking.

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Varsha Suresh, M. Hamza Mughal, Christian Theobalt, Vera Demberg. 2025-10-22. Modeling Turn-Taking with Semantically Informed Gestures. https://arxiv.org/abs/2510.19350

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