Detection of Pronunciation Errors in Arabic Sentences Using LLM with Voice-Based Transformer Models

The Holy Qur'an is not only a holy book for Muslims, but also a text whose correct and beautiful recitation is considered worship. Therefore, the correct pronunciation of Qur'an recitation is of great importance both religiously and educationally. Especially in the digitalized world, the evaluation of audio Qur'an data with automatic methods stands out as an important field of study that can contribute to the development of new generation educational tools and artificial intelligence-based applications. In this study, we propose a system for evaluating the accuracy of the data generated as a result of reading Arabic Qur'an texts aloud. Within the scope of the system, audio data is converted into text using different speech-to-text models and the resulting texts are analyzed with various similarity metrics by comparing them with the reference Quran text. The dataset used includes high-quality audio recordings read by Quran memorizers. Ten different hafiz, each with experience in Qur'anic education, recited the entire Qur'an aloud and these recordings were used as the basic data in the evaluation processes of the system. This study aims to contribute to both the field of language technologies and religious education practices by presenting a new approach to the analysis and evaluation of Arabic Qur'anic recitation with automated methods.