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

2026-07-13

AI Summary

This study addresses the overreliance of current robotic speech recognition systems on online APIs and the lack of a systematic overview of localized deployment strategies. It provides a comprehensive review of integrating automatic speech recognition (ASR) approaches—from traditional methods to state-of-the-art deep learning models such as Whisper—into robotic platforms. For the first time, it systematically analyzes the ecosystem of local ASR applications built upon ROS, cloud-based, and hybrid architectures. By synthesizing major ASR model families, open-source toolkits, and large-scale datasets, this work offers social robotics researchers a practical guide for selecting and deploying speech interaction technologies. It further highlights the core advantages of localized ASR in robustness and privacy preservation, and outlines future directions toward multimodal interaction and operation in dynamic environments.

Abstract

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.

Problem

• speech recognition
• robotics
• local ASR
• human-robot interaction
• deployment

Innovation

• localized speech recognition
• robotic integration
• ROS-based deployment
• hybrid ASR systems
• Whisper