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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/5/249 |
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| author | Kai-Chao Yao Wei-Tzer Huang Hsi-Huang Hsieh Teng-Yu Chen Wei-Sho Ho Jiunn-Shiou Fang Wei-Lun Huang |
| author_facet | Kai-Chao Yao Wei-Tzer Huang Hsi-Huang Hsieh Teng-Yu Chen Wei-Sho Ho Jiunn-Shiou Fang Wei-Lun Huang |
| author_sort | Kai-Chao Yao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0a4208ba655b43abb6c85b14be7d36de |
| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-0a4208ba655b43abb6c85b14be7d36de2025-08-20T02:33:43ZengMDPI AGActuators2076-08252025-05-0114524910.3390/act14050249Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot ControlKai-Chao Yao0Wei-Tzer Huang1Hsi-Huang Hsieh2Teng-Yu Chen3Wei-Sho Ho4Jiunn-Shiou Fang5Wei-Lun Huang6Department of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanThis 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.https://www.mdpi.com/2076-0825/14/5/249speech recognitionDeepSpeech2PythonLabVIEWrobot controldeep learning |
| spellingShingle | Kai-Chao Yao Wei-Tzer Huang Hsi-Huang Hsieh Teng-Yu Chen Wei-Sho Ho Jiunn-Shiou Fang Wei-Lun Huang Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control Actuators speech recognition DeepSpeech2 Python LabVIEW robot control deep learning |
| title | Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control |
| title_full | Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control |
| title_fullStr | Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control |
| title_full_unstemmed | Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control |
| title_short | Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control |
| title_sort | deep learning based speech recognition and labview integration for intelligent mobile robot control |
| topic | speech recognition DeepSpeech2 Python LabVIEW robot control deep learning |
| url | https://www.mdpi.com/2076-0825/14/5/249 |
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