TAJWEED UTOMATION SYSTEM USING HIDDEN MARKOUV MODEL AND NURAL NETWORK

Voice recognition is considered as one of the most important aspects of machine learning and artificial intelligence engineering domain. But it still has limited and modest applications in Arabic language. The Holy Quran is the largest container of Arabic language grammar in terms of speaking and utterance as it is considered as a message for all humanity. However, we present within this study a classification model for four different altajweed rules like the Allah name(mofakham, morakaq) and moon and sun L(لام),as we depended on three different kinds of voice features LPC(liner predictive coding),MFCC(Mel-frequency cepstrum),FFT(Fast Fourier transform),where those three types of features are the most used within the domain of processing voice signaldomain.as we depended on two classifying mechanisms (neural networks and hidden Markov model(HMM)) in order to study all possible cases of those studied rules, then we extracted those features of two different readers(males).each of Markov hidden model and neural networks have been trained by using three different types of extracted features and then we tested those trained models in order to obtain final results as to evaluate them. MATLAB version (0.8.1) , Audacity 2.1.2 cutting the samples voices .will be used to implement this concept to achieve further understanding