这篇综述把机器人语音识别的在线API和本地模型方案都讲透了，尤其适合想用Whisper做离线识别的开发者，ROS集成部分很有实操参考。
本文综述了自动语音识别（ASR）在机器人系统中的集成方法，涵盖在线API和本地模型两种方案。重点分析了从传统方法到OpenAI Whisper等深度学习模型的演变，并列举了LibriSpeech、Common Voice等大规模数据集以及Kaldi、ESPnet等开源工具包。系统梳理了基于ROS、云服务和混合架构的部署策略，以及Pepper、NAO等真实机器人平台的应用案例。最后探讨了在动态嘈杂环境中实现多模态交互的挑战，为社交机器人研究者提供了语言交互领域的导航。
Casting Everything to Online API Services? A Survey of Integrating Localized Speech Recognition Models in Robotic Systems
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.