# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 1 Introduction  
The Arabic language is one of the oldest languages and is characterized due to its uniqueness and flexibility. Among many Semitic languages, Arabic is the most widely spoken language with over 290 million native speakers and 132 million non-native speakers [1]. Arabic is one of the six official languages of the United Nations (UN) [2]. Classical Arabic (CA) and modern standard Arabic (MSA) are the two main dialects of Arabic. CA is the language of the Quran while MSA is its modified version, which is currently used in everyday communication. Rules of pronunciation are very well-defined for CA to preserve the accurate meaning of the words and constitute basic building blocks to help natives as well as non-natives to learn the Arabic language. The requirements to consider for correct pronunciation are the articulation points of the alphabets, characteristics of the alphabets, and extensive practicing of vocals [3]. This research work focuses on developing an automated system that canrecognize the correct pronunciation of the Arabic alphabet. This research is an important milestone for developing and classify a more sophisticated system that can automatically classify words and sentences to help in teaching classical Arabic pronunciation. In this research, we take users' audio data, process, and train neural networks (NN) over this data. The network learns from the data and classifies audio data and hence provides feedback to a learner on the alphabet pronunciation. Automatic speech recognition (ASR) [4] has received considerable attention and recently made significant progress with its applications in mobile computing [5], human-computer interaction [6; 7], information retrieval [8], and assisted communication [9]. ASR is a process of recognizing information from spoken words. Generally, ASR algorithms use acoustic, pronunciation, and language modeling [4; 10]. ASR has active research attention in different human languages [11]. In addition to ASR, many studies performed speech recognition with mispronunciation detection for children and other non-native language learners in many languages, i.e., English [12; 13; 14], and Mandarin Chinese [15; 16]. However, limited work has been done in Arabic ASR using pattern recognition and feature extraction techniques. Pattern recognition techniques used in ASR incorporate hidden Markov model (HMM) [4; 17; 18], Gaussian mixture model (GMM) [4; 19], artificial neural network (ANN) [20], and multi-layer perceptron (MLP) [20] using different feature extraction techniques such as mel-frequency cepstral coefficient (MFCC), linear predictive cepstrum coefficients (LPCC) [21], and spectrogram. The HMM [17; 18] determines the set of states and associates them with the probabilities of transitions between these states called the Markov chain. GMM [19] is a probabilistic model, which represents a normally distributed subclass within a class. Mixture models do not know a data point belonging to a subclass, and it allows the model to learn automatically. During the past decade, a few HMM and NN speech recognition systems have demonstrated to provide higher accuracy in the classical Arabic alphabet and verse recognition tasks. CMU Sphinx is one of the well-known open-source tools for CA based on HMM [22; 23; 24; 25]. Researchers worked on different tasks using HMM such as an 'E-Hafiz system' which was proposed for CA learning using HMM and MFCC as a feature learning technique [26]. This system achieved an accuracy of 92% for men and 90% for children. In [27], the proposed system helps to improve the pronunciation of alphabets using mean square error (MSE) for pattern matching and MFCC as a feature extraction technique. This system successfully recognized correct pronunciation for various alphabets. In [28], the 'Tajweed checking system' demonstrates detection and correction of students' mistakes during recitations using MFCC, and vector quantization (VQ) with an accuracy of 82.1-95%.