A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment
Koji Inoue, Yuki Okafuji, Jun Baba, Yoshiki Ohira, Katsuya Hyodo, Tatsuya Kawahara
Abstract
Turn-taking is a crucial aspect of human-robot interaction, directly influencing conversational fluidity and user engagement. While previous research has explored turn-taking models in controlled environments, their robustness in real-world settings remains underexplored. In this study, we propose a noise-robust voice activity projection (VAP) model, based on a Transformer architecture, to enhance real-time turn-taking in dialogue robots. To evaluate the effectiveness of the proposed system, we conducted a field experiment in a shopping mall, comparing the VAP system with a conventional cloud-based speech recognition system. Our analysis covered both subjective user evaluations and objective behavioral analysis. The results showed that the proposed system significantly reduced response latency, leading to a more natural conversation where both the robot and users responded faster. The subjective evaluations suggested that faster responses contribute to a better interaction experience.
BibTeX
@inproceedings{iros2025_anoiserobustturn,
title = {A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment},
author = {Koji Inoue and Yuki Okafuji and Jun Baba and Yoshiki Ohira and Katsuya Hyodo and Tatsuya Kawahara},
booktitle = {IROS 2025},
year = {2025}
}