Evaluation of the Pronunciation of Tajweed Rules Based on DNN as a Step Towards Interactive Recitation Learning
To address the limitations of manual Tajweed rule assessment in Quranic recitation—namely, scarcity of qualified instructors and low evaluation efficiency—this paper proposes a deep learning–based automatic pronunciation assessment method targeting three core Tajweed rules: Al Mad, Ghunnah, and Ikhfaa. We introduce a novel end-to-end speech classification model that integrates EfficientNet-B0 with the Squeeze-and-Excitation channel attention mechanism. Mel-spectrogram features coupled with normalization-based preprocessing are employed. Evaluated on the public QDAT dataset, the model achieves fine-grained recognition accuracies of 95.35%, 99.34%, and 97.01% for the respective rules. The proposed approach demonstrates high accuracy, strong robustness to acoustic variability, and superior generalization across diverse reciters and recording conditions. It enables instructor-free, self-paced practice with real-time feedback, offering a scalable, deployable technical framework for intelligent religious language education.