Voice-Based Accessibility for Disabled Users in Websites Using Data Mining and Regression Analysis

Abstract

Web accessibility remains a major challenge for users with physical and motor disabilities, as most websites rely on mouse- and keyboard-based interactions that are unsuitable for hands- free navigation. Existing voicebased solutions mainly depend on speech-to-text systems, which require large datasets, high computational resources, and language-specific transcription, lim- iting their effectiveness for lightweight, real-time accessibility support. This research proposes a prediction-based voice accessibility system that enables web navigation without full speech transcription.A dataset of approximately 3000 short audio samples was collected from 30 par- ticipants, covering ten common accessibility commands such as scrolling, zooming, and navi- gation control. MelFrequency Cepstral Coefficients (MFCCs) were extracted as compact audio features, and multiple machine learning classifiers were evaluated. Model performance was as- sessed using stratified train–test splits, cross-validation, precision, recall, F1-score, and confu- sion matrices.A tuned XGBoost classifier achieved an overall accuracy of approximately 72%, outperforming logistic regression, support vector machines, and random forests while main- taining low latency suitable for real-time use. The model was deployed as a browser extension, enabling language-independent, realtime voice-controlled web navigation and improving digi- tal accessibility for disabled users.