# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 2 Related Work
### Pronunciation Detection  
In the direction of pronunciation detection (correct pronunciation and mispronunciation), the previous studies have investigated the machine learning techniques of SVM (support vector machine), KNN (k-nearest neighbors) and NN (neural network) [13], and DNN (deep neural network) techniques of CNN (convolutional neural networks) with transfer learning, AlexNet, BLSTM (bidirectional long short-term memory) [10]. The machine learning algorithms for pronunciation detection had achieved an accuracy of 74.37%for KNN, 83.50% for SVM, and 90% for NN. Similarly, the DNN techniques reported that CNN, AlexNet, and BLSTM give 95.95%, 98.41%, and 88.32% accuracy for recognizing each alphabet, respectively. As well as detecting the quality of pronunciation of each alphabet using CNN, AlexNet, and BLSTM attained the accuracy of 97.88%, 99.14%, and 77.71%, respectively. In DNNs experiments, the dataset was limited to 29 classes of pronunciation, excluding the vowels. Therefore, it is required to fill this gap to support teaching and learning of CA basics. DNN algorithms and models such as CNN consist of multiple hidden layers capable of efficiently extracting important features from a large set of data. These features can be transformed from one layer to the next in a series of several layers with varying weights in the neural network until it results in a set of layers that can be used to initialize a deep learning algorithm for speech recognition [14]. A proposed method of a DNN based on articulatory models, with a multi-label learning scheme, shows promising results in speech error detection [15]. The researchers consider measuring all attributes responsible for generating the sound related to the movement of the tongue, lips, and other organs. This experiment observes 74% of accuracy for pronunciation error detection. We have found significant improvements in pronunciation detection accuracy in DNN techniques compared to machine learning algorithms from the results. Besides machine learning and DNN techniques, pronunciation detection was performed using statistical methods. In [16], a system was proposed to detect how badly an Arabic word was pronounced using different scores of pronunciation measurement. The system used the GLL (Global Average Log Likelihood) score, the LLL (Local Average Log Likelihood) score, the RoS (Rate of Speech score), and the RoA (Rate of Articulation) score to assess the pronunciation quality of the learner quantitatively. The dataset consisted of three Arabic corpora, spoken by six young Algerian learners. The pronunciation of expert learners is used as a benchmark to assess the other five learners. Evaluation measurement was used to decide whether the score calculated by the system could detect mispronunciation; the researchers used the CA (Correct Acceptance), CR (Correct Rejection), FA (False Acceptance), and FR (False Rejection). The results showed that the system could detect mispronunciation, using the GLL score method, with 86.66% of correct rejection, and the GLL had the higher CA + CR (76.66%) and the lower FA + FR (23.32%). In the direction of pronunciation detection of non-native speakers, mispronunciation of Arabic phonemes has been investigated for non-native speakers by analyzing the Arabic speech of Pakistani and Indian speakers from the KSU (King Saud University) database [17]. Research findings of this study highlight that non-native Arabic speakers often mispronounce five Arabic phonemes. The system trained with native and non-native speakers of Arabic phonemes and tested with only non-native speakers. A threshold was set to be compared with the calculated score of GOP (Goodness of Pronunciation) to decide whether the phoneme was pronounced correctly or not. Five experiments were conducted to set up the suitable parameters for the system using HMM (Hidden Markov model). The system used 16 mixtures with 19 HMM re-estimation. Moreover, it extracted 12 MFCC (Mel-frequency Cepstral Coefficients) from sound data.