发表机构
Institute of Science Tokyo; Eindhoven University of Technology（东京科学研究所; 埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
综述自动语音识别技术在机器人系统中的集成，涵盖其从传统到深度学习模型的演变、相关数据集与工具包，介绍基于 ASR 模型家族等的部署策略及实际平台，指出挑战与未来方向，助力社交机器人研究人员探索人机交互领域。
AI 中文摘要
自动语音识别（ASR）已成为现代机器人系统的关键组件，因为它是人类与机器人交互最自然直观的方式之一。常用方法是直接在线使用 API 服务。本文概述了 ASR 技术如何集成到各种智能机器人和机器中。讨论了语音识别从既定方法到诸如 OpenAI 的 Whisper 等深度学习模型的演变。还列出了在工业和学术界广泛使用的大规模数据集和开源工具包。围绕 ASR 模型家族、机器人中的部署策略以及几个实际机器人平台进行综述。最后概述了在机器人中部署强大语音识别的挑战并讨论未来方向，包括在多样动态环境中的多模态交互。本文可帮助社交机器人研究人员更好地探索基于语言的自然人机交互这一新兴领域。
英文摘要
Automatic speech recognition (ASR) has become a critical component of modern robotic systems because it is one of the most natural and intuitive ways for humans to interact with robots. A commonly used method is to directly use API services online. But is that all we can do? This article provides an overview of how ASR technologies are integrated into various intelligent robots and machines. We discuss the evolution of speech recognition from established approaches to state-of-the-art deep learning models, such as OpenAI's Whisper. We also list large-scale datasets and open source toolkits that have been widely used in both industry and academia. We structure the survey around ASR model families, deployment strategies in robotics (especially ROS-based, cloud-based, and hybrid solutions), and several real-world robotic platforms. Finally, we outline the challenges of deploying robust speech recognition in robots and discuss future directions, including multimodal interaction in diverse and dynamic environments. This paper can help social robotics researchers better navigate the emerging domain of language-based natural human-robot interaction.
Commentsaccepted in 18th International Conference on Social Robotics (ICSR + ART 2026)
