解析机器人语音识别的演进与部署挑战，助你选对技术方案。
Casting Everything to Online API Services? A Survey of Integrating Localized Speech Recognition Models in Robotic Systems
- 对比云端API与本地化模型在机器人中的适用场景
- 涵盖Whisper等先进模型及主流开源工具链
- 适合关注人机自然交互的机器人研究者
自动语音识别（ASR）已成为现代机器人系统的关键组件，因其是人类与机器人最自然直观的交互方式之一。当前常用方法是直接调用在线API服务，但这是否是唯一选择？本文综述了语音识别技术在各类智能机器人和机器中的集成现状。讨论了从传统方法到前沿深度学习模型（如OpenAI的Whisper）的演进过程，列举了工业界和学术界广泛使用的大型数据集与开源工具包。文章围绕ASR模型家族、机器人中的部署策略（特别是基于ROS、云和混合方案），以及多个真实机器人平台展开分析。最后，总结了机器人中部署鲁棒语音识别所面临的挑战，并探讨了未来方向，包括在复杂动态环境中实现多模态交互。该论文有助于社会机器人研究人员更好地把握语言驱动的人机交互新兴领域。
原文摘要 · Abstract (English)
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.
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