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arXiv · 2607.23204

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

Yuki Okafuji·Koji Inoue·Yoshiki Ohira
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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.

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Yuki Okafuji, Koji Inoue, Yoshiki Ohira. 2026-07-25. Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot. https://doi.org/0.1145/3776591.3832513

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