Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications
Human-Robot Interaction (HRI) in robots finds wide applications today such as personal assistant robots, autonomous vehicles, healthcare support robots, industrial robots and many more, which receive interpreted commands to perform functions ranging from home automation to real-time assembly line op...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11084769/ |
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| author | Sheeba Uruj Riddhi Goswami Sujala D. Shetty Kalaichelvi Venkatesan Karthikeyan Ramanujam |
| author_facet | Sheeba Uruj Riddhi Goswami Sujala D. Shetty Kalaichelvi Venkatesan Karthikeyan Ramanujam |
| author_sort | Sheeba Uruj |
| collection | DOAJ |
| description | Human-Robot Interaction (HRI) in robots finds wide applications today such as personal assistant robots, autonomous vehicles, healthcare support robots, industrial robots and many more, which receive interpreted commands to perform functions ranging from home automation to real-time assembly line operations. This paper provides a comparative study of different Natural Language Processing (NLP) models that are created by combining advanced Large Language Models (LLMs) with speech processing technologies to create a more intuitive, adaptable, and accurate system for robots. By assessing performance metrics including accuracy, response time and robustness, this paper identifies the best generalized model for the real-world application in robotics. In fact, this framework enables natural language understanding and speech generation to be combined effectively, which can help robots respond quickly to spoken requests, even in dynamic environments. |
| format | Article |
| id | doaj-art-69dff1f44a894e0489a2aedf673831e3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-69dff1f44a894e0489a2aedf673831e32025-08-20T03:32:55ZengIEEEIEEE Access2169-35362025-01-011312717012718210.1109/ACCESS.2025.359059211084769Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World ApplicationsSheeba Uruj0https://orcid.org/0009-0009-9933-0133Riddhi Goswami1https://orcid.org/0009-0009-8244-5858Sujala D. Shetty2Kalaichelvi Venkatesan3https://orcid.org/0000-0002-9144-6846Karthikeyan Ramanujam4https://orcid.org/0000-0001-7401-7698Department of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab EmiratesDepartment of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab EmiratesDepartment of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab EmiratesDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab EmiratesDepartment of Mechanical Engineering, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab EmiratesHuman-Robot Interaction (HRI) in robots finds wide applications today such as personal assistant robots, autonomous vehicles, healthcare support robots, industrial robots and many more, which receive interpreted commands to perform functions ranging from home automation to real-time assembly line operations. This paper provides a comparative study of different Natural Language Processing (NLP) models that are created by combining advanced Large Language Models (LLMs) with speech processing technologies to create a more intuitive, adaptable, and accurate system for robots. By assessing performance metrics including accuracy, response time and robustness, this paper identifies the best generalized model for the real-world application in robotics. In fact, this framework enables natural language understanding and speech generation to be combined effectively, which can help robots respond quickly to spoken requests, even in dynamic environments.https://ieeexplore.ieee.org/document/11084769/HRIrobot operating system (ROS)NLPLLMsspeech-to-text (STT)text-to-speech (TTS) |
| spellingShingle | Sheeba Uruj Riddhi Goswami Sujala D. Shetty Kalaichelvi Venkatesan Karthikeyan Ramanujam Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications IEEE Access HRI robot operating system (ROS) NLP LLMs speech-to-text (STT) text-to-speech (TTS) |
| title | Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications |
| title_full | Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications |
| title_fullStr | Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications |
| title_full_unstemmed | Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications |
| title_short | Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications |
| title_sort | comparative analysis of gpt 4 and llama 3 2 integration with speech processing models for enhancing human x2013 robot interaction and motion control in real world applications |
| topic | HRI robot operating system (ROS) NLP LLMs speech-to-text (STT) text-to-speech (TTS) |
| url | https://ieeexplore.ieee.org/document/11084769/ |
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