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.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20356 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | qZ2k7LbbEBNL |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
