New Hybrid System (Supervised Classifier/HMM) for Isolated Arabic Speech Recognition

In this paper we present experiments we perform in order to recognize Arabic isolated words. Our recognition system is based on the combination of the supervised classifier at the classical system recognition based Markov modeling. This work is an alternative hybrid approach GHMM/supervised classifier (SVM or KNN) used in speech recognition using hidden Markov model with supervised classifier algorithm. The new approach GHMM/SVM or GHMM/KNN is introduced, evaluated and compared with traditional approach GHMM for isolated word recognition system. Both these approaches apply the same principles of feature extraction and time-sequence modeling; the principal difference lies in the additional classifier in the architecture used for training and recognition phases