A Transfer Learning Approach For Identifying Spoken Maghrebi Dialects

This paper investigates a transfer learning approach to solve the spoken dialects identification problem for some under-resourced dialects of the Maghrebi region, including Algerian Arabic Dialect (AAD), Algerian Berber Dialect (ABD), Moroccan Arabic Dialect (MAD), andMoroccan Berber Dialect (MBD). In our experiments, we used different Transfer learning models, namely: Residual Neural Network (Resnet50, Resnet101), and Visual Geometric Group (VGG16, VGG19) using anin-house corpus that we built for each dialect. The corpus is composed of ten digits recorded for each of the aforementioned dialects, repeated ten times by six native speakers. The results vary according to different reasons: the number of epochs, neurons, batch size, and also the datasets combinations used in training and test phases. The best score found is 90.4% by the VGG19 model. Overall, the results show the robustness of our system based on the VGG16 model with an average identification rate of 62.7%.