Development of a System for Collecting Data on the Correct Pronunciation of Classical Arabic Language

The Arabic language has many features such as the phonology and the syntax that make it an easy language for developing automatic speech recognition systems. This paper presents a pronunciation analysis system designed to assist Quran learners in Kyrgyzstan’s madrasas and online platforms. Speech recognition is a captivating process that revolutionizes human-computer interactions, allowing us to interact and control machines through spoken commands. The foundation of speech recognition lies in understanding a given language’s linguistic and textual characteristics. The system records 3-second audio clips, such as “Bismillahir Rahmanir Rahim,” removes noise, segments them into phonetic units (e.g., “Bism,” “Allah”), and evaluates accuracy using a Support Vector Machine (SVM) classifier trained on recordings from QuranicAudio. Feedback is also given in percent so that students can study by themselves. Using Python, Streamlit, Librosa, and scikit-learn, the system has 90–95% percent accuracy identifying correct pronunciation and 60–70% for the incorrect one. The novelty of this work is in combining machine learning based with real hafiz objective recordings, to overcome an absence of available tools for learning Quran pronunciation. Upcoming we plan to extend to more surahs, such as Al-Fatiha and also grow the data size in order to improve accuracy.