Arabic dialect classification using an adaptive deep learning model - Tibi, Nejib; Anouar Ben Messaoud, Mohamed (Bulletin of Electrical Engineering and Informatics, 2025)
In daily life, dialect is the most widely used form of communication. Automatically identifying a dialect is a challenging task, particularly when dealing with similar dialects spoken in the same nation. In this study, we developed an automatic dialect identification of feature extraction based on the deep learning model. First, we extract the cepstral features, the fundamental frequency and glottal instances using our multi-scale product analysis (MPA) of the speech signal. These parameter measurements from the MPA of the speech signal are used as features for the designed Hamilton neural network (HNN) classifier. Our classifier considers both the external and the internal dependencies and allows one to code the dependencies by composing the multi-dimensional features as single entities as well as by determining the correlations between the elements by the recurrent operation. Experimental results show that the proposed dialect identification system achieves significant performance gains compared to current HNN-based approaches. The proposed system is rigorously designed to exploit the strong temporal and spectral relationships of speech, and its components operate independently and in parallel to accelerate processing. In addition, the experimental results indicated the robustness of our deep learning model for the identification of Arabic dialect.