Authors: Mayuresh More, Piyush Punchmukhe , Shantanu Wagh , Rajashree kumbhar
Abstract: Recent developments in conversational Artificial Intelligence have ushered in a new era in the field of natural and easy man–computer interaction. Traditional desktop interfaces are sometimes cumbersome to navigate and operate using the keyboard, potentially making them less accessible and less efficient to use. This paper introduces AURA, an intelligent voice-driven desktop assistant that combines the capabilities of speech recognition, intent understanding using the large language model (LLM), desktop automation, and adaptive voice feedback into a single system. The system uses wake word detection, speech recognition, context-based intent understanding and automatic command execution to allow for hands-free interaction with the desktop. AURA can be used to manage applications, navigate websites, manipulate text, retrieve information and provide conversational assistance using natural language commands. The proposed architecture is realized in Python, with the voice processing and intent analysis modules, command execution, and user interaction management being modularized. Experimental assessment shows that the system interprets the commands accurately, responds quickly to the user and is more user-friendly than traditional command-based systems. The results demonstrate the promise of voice assistants that are powered by LLM for intuitive and inclusive computing experiences. Key Words: Large Language Models, Voice Assistants, Desktop Automation, Human–Computer Interaction, Speech Recognition, Conversational AI, Accessibility.
