Ana Arap lehçeleri arasında ayrım yapmak için bir sistem tasarlama

Dialect identification is one of the more recent areas of interest for scholars. This study focuses on distinguishing between well-known Arabic dialects through conversations or speeches. What distinguishes this study from others is the way it approaches the topic, as speech was treated as if it were a sound, such as the sound of birds or even music. In other words, the networks were trained on everyday speech without delving into the details of the language, which represents a strong challenge for researchers. With this approach, all the difficulties and challenges facing researchers are overcome. Here, three Arabic speech patterns are taken into consideration: Levantine Dialect (LD), Egyptian Dialect (ED), and Arabian Peninsula Dialect (APD). To distinguish between the three talking styles, we suggest three models for artificial neural networks. The Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Deep Recurrent Neural Network (DRNN) networks serve as the foundation for these models. This work presents a thorough analysis of the three suggested models, along with comparisons between them. Spoken Arabic Regional Archive (SARA) dataset is employed. It has been prepared and split up into three sections. The Original SARA (OSARA), Filtered SARA (FSARA), and Mixed SARA (MSARA), which combines the OSARA and FSARA, are these. The suggested DRNN model using the MSARA group of the used dataset has the highest accuracy, 90.70%, according to the results.