Deep Learning Framework For Accurate Quranic Speech Recognition

The paper suggests a deep learning-based architecture of Quranic speech recognition with specific aim to address prosodic and phonetic demands of Quranic recitation. The model entails Tajweed conscious linguistic and phonetic articulation, studying and deduction, evaluation and feedback, ethical and religious supervision and Quranic information. To facilitate benchmarking and reproducible system development in future, it outlines operational pipeline and quantifiable artifacts (orthographic, phonetic and Tajweed outputs). The framework is installed in a way to enable the assessment of education, accessibility and the devoted transmission of Quranic recitation by viewing Tajweed as inner phonological restraint.