Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models
The paper discusses full-duplex spoken dialogue models that can listen and speak simultaneously, enhancing interaction dynamics. The authors investigate how these models synchronize their internal representations during conversation, drawing inspiration from human communication. Their findings indicate strong synchronization under ideal conditions and highlight the models' ability to predict turn-taking through anticipatory cues.
- ▪Full-duplex spoken dialogue models enable simultaneous listening and speaking, mimicking human conversation dynamics.
- ▪The study examines synchronization of internal representations in these models during interaction.
- ▪Results show that representational synchronization is strongest under no noise conditions and that internal states can predict turn-taking.
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Computer Science > Computation and Language arXiv:2605.20356 (cs) [Submitted on 19 May 2026] Title:Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models Authors:Pablo Riera, Pablo Brusco, Cristina Kuo, Marcelo Sancinetti, S.R.K. Branavan View a PDF of the paper titled Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models, by Pablo Riera and 4 other authors View PDF HTML (experimental) Abstract:Full-duplex spoken dialogue models (SDMs) can listen and speak simultaneously, enabling interaction dynamics closer to human conversation than turn-based systems. Inspired by neural coupling in human communication, we study how such models coordinate their internal representations during interaction.
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