# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 2 Related Work
### Audio Features  
In audio classification, features identification and extraction for audio processing is one of the most popular techniques. A system was developed in [19] to recognize the _Makhraj_ (the areas of the mouth from which the Arabic alphabets are pronounced) pronunciation using MFCC features extraction techniques to build a database of features from the audio dataset. Then, the SVM classifier has been used to classify the Arabic alphabet's _Makhraj_ pronunciation. The system is trained with the recorded audio of the Arabic alphabet _Makhraj_ pronunciation. For new input data, the system extracted the features and matched them with the trained data. Then, it was classified and analyzed using the SVM method with RBF (Radial Basis Function) kernel. The audio data used in the research is a collection of 28 Arabic alphabets' audio and 12 features coefficients were extracted to distinguish between _Makhraj_ pronunciation of Arabic alphabet. Different waveforms analysis is used to present the audio data, using audio visualization, FFT (Fast Fourier Transform), and Mel waveform. The result showed that using audio visualization, all letters had a similar representation. On the other hand, using FFT and Mel waveform, each Arabic _Makhraj_ pronunciation showed different representations, which can be used to distinguish between different Arabic alphabets. Another research study proposed a CNN feature-based model to detect mispronunciation in Arabic words [20]. The proposed system extracted features from different layers of the AlexNet network. Researchers collected Quranic verses words that cover all Arabic alphabet letters 30 times by speakers of different ages. The participators of the collected dataset were native/non-native Arabic speakers. After removing the noise from the dataset, it converted to a 2D spectrogram and was used to input the CNN model. Then, discriminative features were extracted from fully connected layers 6, 7, and 8 of AlexNet containing high dimensions features. This method showed a significant result compared with the CNN model. It achieved 85% on the complete Arabic dataset. The MDD (Mispronunciation Detection and Diagnosis) task was performed using the RNN (Recurrent Neural Network) [21] and CTC (Connectionist Temporal Classification) model. The model consisted of five parts, the input layer, which accepts the framewise acoustic features. Then is the convolution, which contains a total of four CNN layers and two max-pool layers. The third part is a bi-directional RNN that captures the temporal acoustic features. The fourth part is MLP layers (Time Distributed Dense layers), which ends with a soft-max layer for the classification output. The last part is the CTC output layer that generate the predicted phoneme sequence. The experiment results showed that the proposed approach significantly outperformed previous approaches. Some researchers used LDA (Linear Discriminant Analysis) to classify the data into the correct class and draw a decision region between the given classes [22]. The first step in this system is preprocessing speech signals, which helps prepare the data for the next processing. The preprocessing includes end-point detection, pre-emphasis, and normalization. The second step is features extraction and uses MFCC techniques to extract different coefficients order 12, 20, and 35. The main contribution of this research is to test a different number of MFCC coefficients with varying percentages of training and testing in LDA. The best performance achieved is 92% for the Arabic phoneme (_Tua_) when using 35 MFCC coefficients and 80% of training data. A recent work uses APDM (Acoustic and Phonetic Decoding Model) for recognizing vowels for naturally uttered MSA-based Gas (Genetic Algorithms) [23]. They use MFCC and the LPC (Linear Prediction Coding) techniques to obtain speech parameters from the speech signal. GA based on Manhattan distance decision rule is applied on several Algerian male and female speakers' recordings and classify phonemes with accuracy of 98.02%.