Voice Laptop Assistant for Handicapped

Voice Laptop Automation for Handicapped Users is an automation system that focuses on accessibility and aims to offer complete and consistent hands-free laptop control through voice commands. Contemporary computer systems are heavily dependent on keyboards and mice, which pose significant difficulties for physically handicapped individuals. The proposed project seeks to overcome these difficulties by allowing users to control a laptop completely via voice commands. The system is built using Python and incorporates highly advanced speech recognition, artificial intelligence, and automation. Voice commands are recorded using a microphone and translated into text using the Speech Recognition library, intelligently processed, and automated using automation libraries such as PyAutoGUI. The system also uses pyttsx3 to offer voice feedback to the user. The application supports functions such as application control, file handling, system commands, text processing, and intelligent AI-based responses using DeepSeek integration. With an average response time of less than 2 seconds and a voice command accuracy rate of over 95%, the system ensures efficient and effective performance. This project is a major milestone in the direction of accessible technology. By leveraging artificial intelligence and voice automation, it enables differently abled individuals to independently, efficiently, and effectively engage with digital technology.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-15
DOI
https://doi.org/10.5281/zenodo.19591965
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Voice Laptop Assistant for Handicapped

M. R. Shaikh, Jaiswar Sahil, Jangale Ashutosh, Jagtap Yash et al.
Zenodo (CERN European Organization for Nuclear Research)
IoT-based Smart Home Systems
article

Voice Laptop Assistant for Handicapped

M. R. Shaikh, Jaiswar Sahil, Jangale Ashutosh, Jagtap Yash, Jaybhaye Harshraj, D.P.Narode
article en

Abstract

Voice Laptop Automation for Handicapped Users is an automation system that focuses on accessibility and aims to offer complete and consistent hands-free laptop control through voice commands. Contemporary computer systems are heavily dependent on keyboards and mice, which pose significant difficulties for physically handicapped individuals. The proposed project seeks to overcome these difficulties by allowing users to control a laptop completely via voice commands. The system is built using Python and incorporates highly advanced speech recognition, artificial intelligence, and automation. Voice commands are recorded using a microphone and translated into text using the Speech Recognition library, intelligently processed, and automated using automation libraries such as PyAutoGUI. The system also uses pyttsx3 to offer voice feedback to the user. The application supports functions such as application control, file handling, system commands, text processing, and intelligent AI-based responses using DeepSeek integration. With an average response time of less than 2 seconds and a voice command accuracy rate of over 95%, the system ensures efficient and effective performance. This project is a major milestone in the direction of accessible technology. By leveraging artificial intelligence and voice automation, it enables differently abled individuals to independently, efficiently, and effectively engage with digital technology.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 9%
IoT-based Smart Home Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.