Resumen
The Arabic language has always been an immense source of attraction to various people from different ethnicities by virtue of the significant linguistic legacy that it possesses. Consequently, a multitude of people from all over the world are yearning to learn it. However, people from different mother tongues and cultural backgrounds might experience some hardships regarding articulation due to the absence of some particular letters only available in the Arabic language, which could hinder the learning process. As a result, a speaker-independent and text-dependent efficient system that aims to detect articulation disorders was implemented. In the proposed system, we emphasize the prominence of ?speech signal processing? in diagnosing Arabic mispronunciation using the Mel-frequency cepstral coefficients (MFCCs) as the optimum extracted features. In addition, long short-term memory (LSTM) was also utilized for the classification process. Furthermore, the analytical framework was incorporated with a gender recognition model to perform two-level classification. Our results show that the LSTM network significantly enhances mispronunciation detection along with gender recognition. The LSTM models attained an average accuracy of 81.52% in the proposed system, reflecting a high performance compared to previous mispronunciation detection systems.