Enhancing Voice Assistant Systems through Advanced AI and NLP Techniques

Rahul  Kumar Singh; Sakshi Kathuria; Pankaj Saraswat; Ashok Kumar

doi:10.70454/JRICST.2025.20110

Authors

Rahul Kumar Singh K. R. Mangalam University, Gurugram, India. Author
Sakshi Kathuria Amity University, Gurugram, India. Author
Pankaj Saraswat Sanskriti University, Mathura, India. Author
Ashok Kumar Teerthankar Mahaveer University, Moradabad, India. Author

DOI:

https://doi.org/10.70454/JRICST.2025.20110

Keywords:

Voice Assistant, Natural Language Processing, Artificial Intelligence , Speech Recognition

Abstract

In the rapidly evolving digital age, voice assistants have become an indispensable tool for enhancing user interaction with technology. This paper explores the design, development, and functionality of a Python-based voice assistant system, leveraging cutting-edge advancements in Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning. The voice assistant is designed to bridge the gap between human commands and machine execution by employing robust speech recognition techniques and advanced contextual understanding. Unlike existing models, the proposed system integrates tone and mood recognition to offer personalized responses and recommendations, thereby elevating user experience. The research delves into significant challenges in the field, such as multi-language adaptability, mood inference, and offline processing capabilities, offering innovative solutions that enhance system reliability and efficiency. By incorporating Python libraries and APIs, the assistant performs diverse tasks, from executing basic commands like opening applications and retrieving weather updates to advanced functionalities like personalized news delivery and automated emotional support. Testing revealed an impressive accuracy rate of 91.87%, demonstrating its practical viability and effectiveness. The findings underscore the growing importance of voice assistants as a transformative technology in the fields of home automation, accessibility, and intelligent systems. This paper aims to contribute to the body of knowledge in AI and NLP, addressing current limitations and setting a foundation for future developments in voice assistant technology.

References

1. Dr. Kshama V. Kulhalli, Dr. M.S. Sheshagiri, and Mr. Abhijit J. Patankar, "PARC: Voice Assistant Research Paper," Available online.

2. Deepak Shende, Ria Umahiya, Monika Raghone, Aishwarya Bhisikar, and Anup Bhange, "AI-Based Voice Assistant Using Python," JETIR.

3. Ms. Ayushi Y. Vadwala, Ms. Krina A. Suthar, Ms. Yesha A. Karmakar, and Prof. Nirali Pandya, "Intelligent Android Voice Assistant: A Future Requisite," IJEDR.

4. Ana Berdasco, Gustavo Lopez, Ignacio Diaz, Luis Quesada, and Luis A. Guerrero, "User Experience Comparison of Intelligent Personal Assistants: Alexa, Google Assistant, Siri, and Cortana," Journal of Computer Technology and Internet Research, Available online.

5. Dr. Marta Perez Garcia Sarica Saffon Lopez Hector Donis, "Everybody is Talking About Virtual Assistants, But How Are Users Really Using Them?" Available online.

6. P. Pradeep, P. Balaji, and S. Bhanumathi, "Artificial Intelligence-Based Person Identification Virtual Assistant," International Journal of Recent Technology and Engineering (IJRTE), Vol. 8, Issue 2S11, September 2019.

7. "Voice Assistants and Artificial Intelligence," SmartSheet, Available online.

8. Singh, R.K., Pandey, M., Sachdeva, K., Shah, S.K., "Automation in Sales Support and its Impact on Supply Chain Management," AIP Conf. Proc., 2023, 2771(1), 020062.

9. Rahul Nijhawan and Rahul Kumar Singh, "Detection of Botrytis Cinerea Using Machine Learning Technique," IEEE International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT-2023), R V Institute of Technology and Management, Bengaluru, 20-21 October 2023.

10. R. Mandal, S. Banerjee, M. B. Islam, P. Chatterjee, and U. Biswas, "QoS and Energy Efficiency Using Green Cloud Computing," in Intelligent Internet of Things for Healthcare and Industry, Springer, 2022, pp. 287–305, ISBN: 978-3-030-81472-4. DOI: 10.1007/978-3-030-81473-1_14.

11. R. Mandal, M. K. Mondal, S. Banerjee, C. Chakraborty, and U. Biswas, "A Survey and Critical Analysis on Energy Generation from Data Center," in Data Deduplication Approaches, Elsevier, 2021, pp. 203–230.

12. Mohd Junedul Haque and Rahul Kumar Singh, "A Lossless Steganography Framework: Implementation and Evaluation," IEEE International Conference on Advances in Electrical, Electronics, and Computational Intelligence (ICAEECI 2023), K.S.R. College of Engineering, Tamil Nadu, India, 19-20 October 2023.

13. Rahul Kumar Singh et. al Task Offloading in Fog-Assisted Cloud Environment Using M/G/G/K/K+2 Queuing for Smart Homes; HTL JOURNAL Volume 30, Issue 12, DECEMBER 2024.