DIACRITIC-AWARE ALIGNMENT AND CLASSIFICATION IN ARABIC SPEECH: A FUSION OF FUZTPI AND ML MODELS

Sabour, Adel (Adel Sabour Computer Science and Systems School of Engineering and Technology University of Washington, Tacoma Tacoma, WA, USA)
Hendawi, Abdeltawab (Abdeltawab Hendawi Computer Science and Data Science University of Rhode Island Kingston, RI, USA)
Ali, Mohamed (Mohamed Ali Computer Science and Systems School of Engineering and Technology University of Washington, Tacoma Tacoma, WA, USA)

JISTech (Journal of Islamic Science and Technology)
Vol 8, No 2 (2023)

This paper presents the Quran Speech Recognition (QRSR) system, achieving alignment and classification accuracies up to 96%. The system is designed to advance Arabic Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) by focusing on the Arabic diacritic-annotated text. We address the limitations of existing Arabic ASR systems and introduce the Fuzzy Text Alignment and Rule-based Classifier (FTARC) for segmenting audio files and aligning text. The FuzTPI algorithm is integrated with Machine Learning models like Na¨ıve Bayes, Support Vector Machine, and Random Forest. This research aims to generalize the findings for broader Arabic text and contribute to an expanded audio dataset, thereby enhancing Arabic NLP and speech recognition capabilities.