Virtual Assistant: JarvisAI Using Natural Language Processing

Year : 2025 | Volume : 03 | Issue : 02 | Page : 26 39
    By

    Anamika Rana,

  • Sushma Malik,

  • Gaurav Sehgal,

  • Khushi Malhotra,

  1. Associate Professor, Department of Computer Applications, Maharaja Surajmal Institute of Technology, Higher Educational Institution, Delhi, India
  2. Assistant Professor, Department of Computer Applications, Maharaja Surajmal Institute of Technology, Higher Educational Institution, Delhi, India
  3. Student, Department of Computer Applications, Maharaja Surajmal Institute of Technology, Higher Educational Institution, Delhi, India
  4. Student, Department of Computer Applications, Maharaja Surajmal Institute of Technology, Higher Educational Institution, Delhi, India

Abstract

This research presents the development of a voice-interactive virtual assistant built upon the JarvisAI framework, integrating advanced technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Speech Recognition. The goal is to enable seamless and intuitive human-computer interaction by allowing users to communicate through natural spoken and written language. The assistant is designed to understand, interpret, and respond to various user commands, aiding in tasks such as information retrieval, task automation, and improving overall productivity. The architecture of JarvisAI consists of three primary components: a robust speech recognition system, an intent recognition engine for understanding user input, and a text-to-speech (TTS) module for voice-based responses. A transformer-based language model (GPT-3) is employed for contextual comprehension and semantic processing, while an integrated knowledge retrieval system ensures the delivery of accurate and relevant answers. Furthermore, the assistant incorporates personalized recommendation features to enhance user engagement and experience. Initial performance assessments indicate high accuracy, low latency, and strong user satisfaction. However, the system also faces challenges such as handling ambiguous commands, maintaining contextual continuity over time, and ensuring data privacy. Future enhancements aim to support multilingual communication and emotionally intelligent interactions, further broadening the assistant’s accessibility and human-like responsiveness.

Keywords: Voice-enabled virtual assistant, natural language processing, machine learning, speech recognition, intent recognition engine, Text-to-speech module, contextual understanding

[This article belongs to International Journal of Computer Science Languages ]

How to cite this article:
Anamika Rana, Sushma Malik, Gaurav Sehgal, Khushi Malhotra. Virtual Assistant: JarvisAI Using Natural Language Processing. International Journal of Computer Science Languages. 2025; 03(02):26-39.
How to cite this URL:
Anamika Rana, Sushma Malik, Gaurav Sehgal, Khushi Malhotra. Virtual Assistant: JarvisAI Using Natural Language Processing. International Journal of Computer Science Languages. 2025; 03(02):26-39. Available from: https://journals.stmjournals.com/ijcsl/article=2025/view=232865


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Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 01/03/2025
Accepted 17/03/2025
Published 08/09/2025
Publication Time 191 Days
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