Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot

Abstract:Large language model (LLM)-based dialogue systems suffer response delays because generation begins only after final speech recognition. While fixed fillers are a workaround, they become unnatural over time. We propose a two-stage incremental framework that decouples prefatory-response preparation from speech onset. Once user intent becomes predictable, an intent readiness detector triggers LLM-based generation of a short prefatory response. Concurrently, a voice activity projection (VAP) model determines when to deliver it. Through a field experiment with a route-guidance robot in a shopping mall, we evaluated three conditions: no-filler, fixed-filler, and contextual-preface. Both fixed-filler and contextual-preface significantly reduced initial response latency relative to no-filler. Relative to fixed-filler, contextual-preface had significantly longer initial response latency but a significantly shorter initial-to-main gap. Exploratory ratings showed no significant differences. These results indicate a timing trade-off.

5 pages, 4 figures. Accepted at ICMI LBR 2026

Robotics (cs.RO); Computation and Language (cs.CL); Sound (cs.SD)

arXiv:2607.23204 [cs.RO]

(or arXiv:2607.23204v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.23204 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

0.1145/3776591.3832513 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.