Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control

This study implemented an innovative system that trains a speech recognition model based on the DeepSpeech2 architecture using Python for voice control of a robot on the LabVIEW platform. First, a speech recognition model based on the DeepSpeech2 architecture was trained using a large speech dataset, enabling it to accurately transcribe voice commands. Then, this model was integrated with the LabVIEW graphical user interface and the myRIO controller. By leveraging LabVIEW’s graphical programming environment, the system processed voice commands, translated them into control signals, and directed the robot’s movements accordingly. Experimental results demonstrate that the system not only accurately recognizes various voice commands, but also controls the robot’s behavior in real time, showing high practicality and reliability. This study addresses the limitations inherent in conventional voice control methods, demonstrates the potential of integrating deep learning technology with industrial control platforms, and presents a novel approach for robotic voice control.