# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 6 Discussion  
This research addresses the recognition of short vowel phonemes in the Arabic language. The recognition task falls into the classification of classical Arabic words, a dialect of the Arabic language that is very old but is understood and applied to this day by millions of  
Figure 4: (**a**) training and validation accuracy and (**b**) training and validation loss of the optimized CNN model on the augmented dataset. Figure 5: Scatter graph showing classes misclassified as other classes. people around the world. Our work is one step ahead of the previous efforts in classifying a total of 84 Arabic short vowels. The authors in [10] achieved an accuracy of 97.88% using CNN for Arabic alphabets (28 classes) recognition without considering the vowels. We believe that the authors could achieve highly accurate results because without considering the vowel phonemes the chances of interclass misclassification are reduced significantly. [PERSON] and [PERSON] [30] have considered formants and consonant-vowels-consonant (CVC) utterances for identifying similarities and differences of vowels for MSA, but to the best of our knowledge, there is no work for CA vowels recognition despite their similarities with the MSA [12]. This research started with data collection followed by its augmentation through various techniques, which is lacking in the previous studies. In [9; 10; 20; 50], the authors have contributed to the basic Arabic alphabets audio data collection, and they mostly performed manual feature extraction. In [51], the authors collected the audio dataset of Arabic words. However, [PERSON] et al. [39] investigated Arabic short vowels' properties that helped us understand their characteristics and duration variations. We were inspired by this approach and applied data augmentation to our collected audio files. Our model supports the automatic classification of Arabic short vowels on 84 classes using deep learning neural networks instead of manually identifying the audio features as done in the studies [10; 23; 26]. The data augmentation helped us to achieve accuracy above 95%, as in the previous research the authors have used data augmentation on 28 classes of Arabic alphabet dataset, and improved DCNN model's accuracy from 65.89% to 95.95%, Alexnet's model accuracy from 78.03% to 98.41%, and BLSTM's model accuracy from 53.18% to 87.90%[10]. In the previous studies, the authors have identified different audio features for the classification of Arabic alphabets [10; 18] into 28 classes, and Arabic words [2; 7; 25; 29] using the CNN model. The studies in [13; 17; 19; 20; 26] identified appropriate features of Arabic audio for applying machine learning techniques [17; 23; 50] for classification purposes. Mispronunciation detection of Arabic is a significant parameter in a Computer Assisted Language Learning (CALL) system [51]. This is mainly a problem for non-native speakers, and approaches like [52] try to detect confusing Arabic pronunciation of similar-sounding letters for non-native speakers. However, this problem even exists in the Arabic-speaking world due to the prevalence of different regional dialects in the various parts of the world. Our proposed approach can be utilized for developing CNN models in a similar domain for learning support systems. This approach helps construct CNN models from scratch and improves them by applying various techniques of data augmentation, fine-tuning neural networks, and hyperparameters tuning. Similar methods are used in other domains [34] for CNN models improvements. Given that the model's performance improved with synthetic data, there are chances of achieving high accuracy if more real data can be retrieved. Given our experience in this research, we believe that it will be helpful for the researchers to save their time and make these processes simple by reducing the complexity of audio data preprocessing by bypassing features identification steps.