# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 1 Introduction
In [29], a 'Qalqalah letter pronunciation' is proposed using spectrogram, this technique illustrates the mechanism of Qalqalah sound pronunciation. In [30], a'mispronunciation detection system for Qalqalah letters' is proposed using the MFCC, and support vector machine (SVM) classifier, which provides an accuracy of 97.5%. Deep learning (DL) algorithms learn a hierarchical representation from data with numerous layers [31]. The hidden layers are responsible to extract important features from the raw data to achieve a better representation of the audio data. In [32], the author proposes an Arabic alphabet recognition model using RNN with back-propagation through time, with an accuracy of 82.3% tested for 20 alphabets. In recent research [33], the authors demonstrated a mispronunciation detection system using different handcrafted techniques for feature extraction and SVM, KNN, and NN as classifiers. This experiment achieved an accuracy of 74.37% for KNN, 83.90% for SVM, and 90.1% for NN. In this paper, we propose a DL algorithm, i.e., DCNN, AlexNet, and BLSTM neural networks, for the Arabic alphabet classification. Our research is different from the previous works in terms of the dataset, features extraction, network architecture, and performance. We employ mel-spectrogram for extracting features of the audio dataset of alphabets. The mel-spectrogram is the conversion of audio frames into frequency-domain representation, which are scaled on an equally spaced mel-scale. The magnitude or power spectrum passes through the mel-filter to obtain the mel-spectrogram. The previous approachesmostly use MFCC, which is related to mel-spectrogram. MFCC coefficients are obtained by passing a mel-spectrum through a logarithmic scale and then discrete cosine transform (DCT). Due to excessive use of DL in speech systems, DCT is no longer a necessary step [34]. We are working on two classification problems using an audio dataset of the Arabic alphabet. The first problem is a multi-class classification task to detect and classify the alphabet to their respective classes. The second problem is a binary class classification task that detects and classifies the correct and incorrect pronunciation to their respective class. The models we use in this research are CNN model learns features from mel-spectrogram and BLSTM learns to use the spectral features technique. In this paper, our major contributions are:  
1. Collection of an audio dataset for the Arabic alphabet with correct and mispronunciation. 2. Arabic alphabet classification (recognize each alphabet). 3. Arabic alphabet pronunciation classification (detect correct pronunciation of the alphabet). 4. Exploration of DCNN, AlexNet, and BLSTM to perform classification of the audio set of the Arabic alphabet. The Arabic language has 29 alphabets, and we consider each alphabet as a class. The Arabic alphabet classification is a multi-class classification problem, which involves the Arabic alphabet audio dataset, and the classification task recognizes each alphabet's class. The audio file with the alphabet sequence shown in Figure 1, is fed into NN. The network learns and extracts the feature set of each alphabet based on its characteristics. The classifier then evaluates and differentiates the alphabet in their respected class. On the other hand, Arabic alphabet pronunciation classification is a binary classification problem. In this task, our focus is to detect the correct pronunciation of the alphabet. The network learns characteristics of the dataset and classifies them into correct pronunciation and mispronunciation classes. The organization of the rest of the paper is as follows. Section 2 explains the collection and preprocessing of the data. Section 3 presents the proposed methodology and DL classification models. In Section 4, we present experimental results and their comparative analysis. Section 5 concludes our work.