Sign-Bridge: A Dual Sign Language Translator App for Enhanced Communication and Inclusivity

Abstract

This study presents the development of Sign-Bridge, a dual-mode mobile application to enhance communication accessibility for Deaf students enrolled in the university by supporting Filipino Sign Language (FSL). Unlike most existing solutions that default to American Sign Language (ASL), this app is purpose-built for FSL, reflecting local linguistic and cultural context. Employing a developmental research framework and the Agile Software Development Life Cycle (SDLC), the system was engineered using Android Studio, Flutter, Node.js, Teachable Machine, and TensorFlow Lite. The Sign-Bridge app features two-way translation: it converts spoken and written text into FSL gestures through deep learning-based recognition and translates FSL signs into text. The evaluation involved 12 purposively selected participants: five IT experts who assessed system performance and maintainability, one content expert who validated linguistic accuracy, and six Deaf students who evaluated its usability. McCall’s Software Quality Model concluded the expert assessments, while the Technology Acceptance Model (TAM) guided user feedback. The system achieved 100% accuracy in text-to-sign and audio-to-sign translation, and 90% accuracy in sign-to-text recognition. Expert evaluators gave an average rating of 4.39 (Good), while users rated the app 4.18 (Agree), indicating a high level of technical soundness and user satisfaction. These results demonstrate the Sign Bridge app’s strong potential as an inclusive educational and communication tool, contributing meaningfully to accessible technology development for the Deaf in the Philippine context.