実環境対話ロボットのための雑音に頑健なターンテイキングシステム：フィールド実験
A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment
Transformerベースの雑音に強いVAPモデルを提案し、ショッピングモールでの実証実験で従来のクラウド音声認識より応答遅延を減らし自然な会話を実現した。
著者: Koji Inoue, Yuki Okafuji, Jun Baba, Yoshiki Ohira, Katsuya Hyodo, Tatsuya Kawahara
分類: cs.RO, cs.CL, cs.SD
原文アブストラクト
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.
