## 3 Methodology  
Figure 1 depicts the Arabic region-wise speech recognition and classification model labeled in two phases. In phase 1, the Klaam library processes 4071 Arabic audio clips from five major dialects (MSA, Egyptian, Gulf, Levantine, and North African) to appropriately identify speech. The pre-trained model from the Klaam library was selected because of its notable benefits in recognizing Arabic dialects. It has a solid foundation because it was specially designed and pre-trained on a large quantity of Arabic speech data, enabling it to capture the complex phonetic and prosodic nuances necessary for differentiating Klaam  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Literature** & **Model Used** & **Scope** & **Data Used** & **Results** \\
**Reference** & & & & \\ \hline \multirow{4}{*}{2018 [2]} & 1. Long short-term memory & & & \\  & (LSTM) & & & \\  & 2. Convolutional neural & & & \\  & networks (CNNs) & & & \\  & 3. Bi-directional LSTM & & & \\  & (BLSTM) & & & \\  & 4. Convolutional LSTM & & & \\  & 2022 [5] & CNN & & \\  & 2018 [4] & CNN & Neural network for Arabic & & \\  & & &  
models that have shown state-of-the-art or extremely competitive performance in related Arabic speech tasks. Additionally, by exploiting the model's prior understanding of Arabic speech, using this pre-trained model made transfer learning easier, allowing for faster convergence, better generalization, and less need for incredibly huge bespoke datasets for each dialect. It also has an open-source community. We sought to improve our dialect detection abilities by using a model designed specifically for Arabic.  
The MFCC feature extraction method is applied for feature extraction, and separated transcriptions are further examined using techniques such as Kendall's Tau, Spearman's correlation, and K-Best with ANOVA to choose the superlative features. In phase 2, the focus is on region-wise dialect classification, where feature selection and data augmentation are conducted using techniques such as SMOTE, random oversampling, and ADASYN. CNNs with different architectures (1D and 2D CNNs) and ANNs are deep learning models trained using augmented data. Parameters such as accuracy, precision, recall, and F1-score are simulated to gauge the trained models, and their performances are assessed against raw sample audio data. To correctly identify and categorize Arabic dialects from diverse geographical locations, this study elaborates on an architecture combining linguistic analysis and deep learning techniques, ultimately advancing NLP for the Arabic language. One-dimensional and two-dimensional convolutional neural networks (CNNs) are employed in dialect classification due to their compatibility with the input data format and their proficiency. A 1D CNN is optimal for sequential input, such as raw audio waveforms or text sequences, by employing filters along a single dimension to extract local temporal or sequential patterns, such as distinct phonemes, intonations, or word sequences that are indicative of various languages. A 2D CNN is specifically developed for two-dimensional data, such as spectrograms, which visually depict audio signals and illustrate the variation in frequency content over time. Using filters along both temporal and spectral dimensions, 2D CNNs can acquire intricate time-frequency patterns and prosodic characteristics that differentiate dialects. Both varieties of CNNs autonomously acquire hierarchical features from the data, diminishing the necessity for manual feature engineering. Performance evaluation criteria, including accuracy, precision, recall, F1-score, and the Matthews correlation  
Figure 1: Basic block diagram for region-wise Arabic speech recognition and classification.  
coefficient, were employed in our study to analyze the performance of various models. Figure 2 represents the research flow diagram.