Towards an End-to-End Speech Recognition Model for Accurate Quranic Recitation

Speaking is the most natural way of communicating with others. Speech recognition technology aims to enable machines to comprehend and act upon human speech. In the field of education, speech recognition has proven to be a valuable tool for automatic language correction, particularly for Quran recitation. Correct recitation of the Quran is crucial for Muslims, and traditionally, it requires an expert "gari" to identify and correct mistakes. While this method is effective, it’s also time-consuming. Quranic speech recognition apps can help students with their recitation, but they require a robust and accurate speech recognition model. While recent models for Arabic and non-Arabic speech recognition have been successful, research on Holy Quran speech recognition is still in its early stages. As such, this study aims to propose an end-to-end deep learning model for Holy Quran speech recognition using the ESPnet toolkit.