Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot
Researchers propose a two-stage framework for LLM-based dialogue robots that generates context-aware prefaces before speech onset, reducing response latency. Field experiments show timing trade-offs between filler types.
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[Submitted on 25 Jul 2026]
Title:Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot
View a PDF of the paper titled Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot, by Yuki Okafuji and 2 other authors
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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.
Comments: 5 pages, 4 figures. Accepted at ICMI LBR 2026
Subjects:
Robotics (cs.RO); Computation and Language (cs.CL); Sound (cs.SD)
Cite as: arXiv:2607.23204 [cs.RO]
(or arXiv:2607.23204v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.23204
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
0.1145/3776591.3832513
DOI(s) linking to related resources
Submission history
From: Yuki Okafuji [view email] [v1] Sat, 25 Jul 2026 13:54:50 UTC (421 KB)
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