Casting Everything to Online API Services? A Survey of Integrating Localized Speech Recognition Models in Robotic Systems

Sheng Li, Jing Li, Felix Schijve, Jun Hu, Emilia Barakova

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.