HMM/GMM classification for articulation disorder correction among algerian children

In this paper, we propose an automatic classification for Arabic phonemic substitution using a Hidden Markov Model/Gaussian Mixture Model (HMM/GMM) systems. The main objective is to help Algerian children in the correction of articulation problems. Five cases are analyzed in the experiments, 20 Arabic words are recorded by a 20 Algerian children, with age range between 4 and 6 years old. Signals are recorded and stored as wave format with 16kHz as sampling rate, 12 Mel Frequency Cepstral Coefficients (MFCC), with their first and second derivates, respectively ∆ and ∆∆ are extracted from each signal and used to the training and recognition phases. The proposed system achieved its best accuracy recognition 85.73%, with 5-stats HMM when the output function is modelled by a GMM with 8Gaussian components.