Deep Learning-Based Pronunciation Detection of Arabic Phonemes

Nowadays, detection of mispronunciations and providing feedback to users are provided by systems called computer-assisted language learning (CALL). In this study, we propose a model that detects the correct pronunciation of Arabic phonemes and supports Arabic language learning by blending it with deep and hybrid deep learning algorithms and mel-frequency cepstrum coefficients (MFCC) and Mel spectrogram feature extraction methods. In order to demonstrate the performance of the proposed model, 29 letters in the Arabic alphabet, 7 of which are hafiz, are voiced by a total of 10 different people. The amount of data has been increased by using the methods of adding noise, time shifting, time stretching, pitch shifting, and its performance on the model has been observed. Experiment results show that the combination of Mel-spectrogram and ConvLSTM exhibit the best results with 97.3% of accuracy when compared with the studies in the literature.