Towards developing impairments arabic speech dataset using deep learning

Abstract

The effective and efficient recognition of speech sounds errors for impaired children is important if a defective phonological process is early detected and corrected. This study deals with the topic of classification of speech sound errors in Arabic impairments children when Arabic letters and numbers are incorrectly pronounced. For 18 standard Arabic isolated numerals and characters, we created an impaired children speech recognition system. We utilized the Mel frequency cepstral coefficients throughout the feature extraction step. then deep long short-term memory network recognition phase. We used the developed model with the developed dataset and the classification accuracy was 97.99% and lose 0.18%, additionally, the results have been compared and yielded extremely intriguing results with previously existing recognition rates models.