ロボットと話す：HRI応用のための音声基盤モデルの実践的検討
Talking to Robots: A Practical Examination of Speech Foundation Models for HRI Applications
人間とロボットの対話で使われる音声認識システム4種を、雑音・訛り・子供/高齢者・障害・自発発話など6つの難しさを持つ8データセットで評価し、性能差や幻覚・バイアスの問題を明らかにした。
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著者: Theresa Pekarek Rosin, Julia Gachot, Henri-Leon Kordt, Matthias Kerzel, Stefan Wermter
分類: cs.RO, cs.AI, cs.CL, cs.HC
原文アブストラクト
Automatic Speech Recognition (ASR) systems in real-world settings need to handle imperfect audio, often degraded by hardware limitations or environmental noise, while accommodating diverse user groups. In human-robot interaction (HRI), these challenges intersect to create a uniquely challenging recognition environment. We evaluate four state-of-the-art ASR systems on eight publicly available datasets that capture six dimensions of difficulty: domain-specific, accented, noisy, age-variant, impaired, and spontaneous speech. Our analysis demonstrates significant variations in performance, hallucination tendencies, and inherent biases, despite similar scores on standard benchmarks. These limitations have serious implications for HRI, where recognition errors can interfere with task performance, user trust, and safety.
