Emotion-Aware AI-Integrated Sign Language System Using Quantum Machine Learning for Inclusive Multimedia Communication

Emotion in communication is a fundamental bridge of human connection. Existing accessibility systems for deaf and mute individuals rely mainly on subtitles, which fail to convey emotional depth. This proposal introduces an AI-integrated sign language system enhanced with Quantum Machine Learning (QML) to capture both meaning and emotional expression. The system focuses on multi-modal features such as gestures, facial expressions, and motion dynamics, enabling real-time translation and integration into multimedia platforms. The research aims to promote inclusive communication and normalize sign language usage in mainstream media. The proposed framework will employ deep learning and quantum-inspired feature representation techniques to improve the accuracy and efficiency of sign language recognition. It will analyze the relationship between linguistic gestures and corresponding emotional states to generate more context-aware translations. The system will also explore real-time processing capabilities to support applications such as educational platforms, video conferencing, digital entertainment, and public communication. Furthermore, the integration of QML is expected to enhance complex pattern recognition and classification of high-dimensional multimodal data. The anticipated outcome is an intelligent and scalable communication framework that reduces accessibility barriers and enables more natural, expressive, and emotionally meaningful interaction between sign language users and the wider community.