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.
View on arXiv@article{inoue2025_2503.06241,
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 },
journal={arXiv preprint arXiv:2503.06241},
year={ 2025 }
}