AI-Powered Voice Assistant for Accessibility Applications

Abstract

AI-powered voice assistants are revolutionizing accessibility applications by offering intuitive, hands-free interaction for individuals with disabilities. These technologies enable users to manage tasks, control smart devices, and enhance their overall experience, fostering inclusivity and independence. With features such as natural language processing (NLP), these assistants understand and conversationally respond to spoken commands, making interactions seamless and user-friendly. They can learn individual preferences, providing personalized experiences that enhance engagement. Additionally, their integration with smart home devices allows users to easily manage their environments, while support for multiple languages ensures accessibility for diverse linguistic backgrounds. In various industries, such as healthcare, education, retail, and transportation, AI voice assistants streamline processes, improve communication, and facilitate real-time assistance, ultimately transforming the quality of life for individuals with disabilities. By promoting independence and enhancing user experiences, these voice assistants are invaluable tools in the pursuit of a more inclusive society.

Introduction

Speech recognition is a transformative technology that is gradually replacing traditional input methods like keyboards and mice. It enables users — especially those with physical disabilities — to interact with devices using their voice, supporting applications like smart homes, task automation, and virtual assistance.

Key Highlights:

• Early Development: Speech recognition efforts began in the 1950s, aiming to make computers understand human speech.

• Modern AI Integration: Today’s AI-based voice assistants (e.g., Siri, Alexa, Google Assistant, Bixby) use natural language processing (NLP) and machine learning (ML) to recognize and respond to user commands.

• User Benefits: These assistants help with information retrieval, smart device control, and personal productivity, particularly benefiting users with functional impairments.

Objectives:

• Develop a personal voice assistant for Windows systems (like Jarvis) that takes input via voice or text.

• Use speech recognition and AI to perform tasks like opening apps, answering questions, or automating system operations.

• Improve accessibility and usability for all users, especially those with disabilities.

Literature Review Insights:

• There has been significant innovation in voice recognition due to the rise of smart devices (phones, watches, speakers).

• Machine Learning and Big Data have improved voice assistant performance by enabling systems to learn from usage data.

• Assistants process voice commands by:

  1. Converting speech to text.

  2. Extracting key phrases.

  3. Executing tasks based on recognized commands.

Methodology:

• The assistant (like Jarvis) uses AI, ML, and speech recognition to understand commands.

• It recognizes phonemes (smallest units of sound) to interpret user speech.

• The system design includes:

  • Input design: Efficient, error-free data entry via speech.

  • Output design: Displays results clearly to aid decision-making and improve usability.

• Examples shown include voice commands to open apps like YouTube and Google.

Conclusion

With just one query, the voice assistant can automate several services, making it easier for users to do things like search the web, get the weather forecast, and access applications like Instagram, Facebook, and the calculator. This method verifies speakers by looking at the unique information in their speech signals. The goal of the project is to become a full server assistant, taking over all server management tasks. In the future, we want to connect Jarvis to mobile devices using React Native so that users may have the same experience on both platforms. The present version offers strong features and is responsive, however it might be better at comprehending and being reliable. The long-term plan is to combine NLP, machine learning, and IoT technologies to get better outcomes.

References

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Copyright

Copyright © 2025 J. Karthik, A. Vishnu Chandra, P. Mani Sai, Swapna Kamakshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.