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Volume 14
Issue 5
10.3390/act14050249
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Kai-Chao Yao
1,2,3,4,
Wei-Tzer Huang
1,2,4,
Hsi-Huang Hsieh
1,5,
Teng-Yu Chen
1,*,
Wei-Sho Ho
1,6,*,
Jiunn-Shiou Fang
1,2,4 and
Wei-Lun Huang
1,7
1
Department of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, Taiwan
2
Master’s Degree Program in Artificial Intelligence Techonology Application, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, Taiwan
3
Kenda Cultural and Educational Foundation, No. 146, Sec. 1, Zhongshan Rd., Yuanlin City 510037, Taiwan
4
Department of Electrical and Mechanical Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua City 500208, Taiwan
5
Yaw Shuenn Industrial Co., Ltd., No. 7, Aly. 6, Gongye Ln., Fengzheng Rd., Nanshi Vil., Wufeng Dist., Taichung City 413001, Taiwan
6
NCUE Alumni Association, National Changhua University of Education Jin-De Campus, No. 1, Jinde Rd., Changhua City 500207, Taiwan
7
Medical Affairs Office, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Zhongzheng Dist., Taipei City 100225, Taiwan
*
Authors to whom correspondence should be addressed.
Actuators 2025, 14(5), 249; https://doi.org/10.3390/act14050249
Submission received: 19 February 2025 / Revised: 30 April 2025 / Accepted: 13 May 2025 / Published: 15 May 2025
(This article belongs to the Section Actuators for Robotics)
Versions Notes

Abstract

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.
Keywords: speech recognition; DeepSpeech2; Python; LabVIEW; robot control; deep learning speech recognition; DeepSpeech2; Python; LabVIEW; robot control; deep learning

Share and Cite

MDPI and ACS Style

Yao, K.-C.; Huang, W.-T.; Hsieh, H.-H.; Chen, T.-Y.; Ho, W.-S.; Fang, J.-S.; Huang, W.-L. Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control. Actuators 2025, 14, 249. https://doi.org/10.3390/act14050249

AMA Style

Yao K-C, Huang W-T, Hsieh H-H, Chen T-Y, Ho W-S, Fang J-S, Huang W-L. Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control. Actuators. 2025; 14(5):249. https://doi.org/10.3390/act14050249

Chicago/Turabian Style

Yao, Kai-Chao, Wei-Tzer Huang, Hsi-Huang Hsieh, Teng-Yu Chen, Wei-Sho Ho, Jiunn-Shiou Fang, and Wei-Lun Huang. 2025. "Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control" Actuators 14, no. 5: 249. https://doi.org/10.3390/act14050249

APA Style

Yao, K.-C., Huang, W.-T., Hsieh, H.-H., Chen, T.-Y., Ho, W.-S., Fang, J.-S., & Huang, W.-L. (2025). Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control. Actuators, 14(5), 249. https://doi.org/10.3390/act14050249

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