Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot
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-f...
| Authors | Yuki Okafuji, Koji Inoue, Yoshiki Ohira |
|---|---|
| Primary category | cs.RO |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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