Fast When, Careful Who: Dual-Process Multiparty Turn-Taking with Diffusion Augmentation

Abstract:Reliable turn-taking is essential for spoken dialogue systems. However, most existing methods are designed for two-speaker interaction and struggle with realistic multiparty audio containing overlap and rapid speaker changes. We study multiparty turn-taking on the VoxConverse dataset and propose an audio-only two-stage pipeline that separates when to trigger a turn boundary from whether the floor is actually transferring. A fast trigger scans the audio and proposes candidate end-of-turn times, while a lightweight verifier runs only at those times to decide \textsc{Hold} or \textsc{Shift} and support next-speaker prediction. We report results in the full multiparty setting and a controlled dyadic top-2 projection for comparability. We also investigate diffusion-based, label-preserving background-audio mixing as a data augmentation strategy. Results show improved shift detection over a baseline, with further improvements from diffusion augmentation.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2606.16568 [cs.CL]

(or arXiv:2606.16568v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.16568 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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