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

Adel Sabour, Abdeltawab Hendawi, Mohamed Ali

Abstract


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.

Keywords


The Arabic Annotated text ; Machine Learning; Classification algorithms; Audio segmentation; Text-audio alignment; Speech Recognition;

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References


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DOI: http://dx.doi.org/10.30829/jistech.v8i2.17951

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