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 | Zendy

Sheeba Uruj | Zendy; Riddhi Goswami | Zendy; Sujala D. Shetty | Zendy; Kalaichelvi Venkatesan | Zendy; Karthikeyan Ramanujam | Zendy

Open Access

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

Author(s) -

Sheeba Uruj,

Riddhi Goswami,

Sujala D. Shetty,

Kalaichelvi Venkatesan,

Karthikeyan Ramanujam

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3590592

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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