SOUND RECOGNITION FOR SELECTED VERSES OF THE HOLY QURAN

Abstract

In this study of sound recognition for selected verses of the Holy Quran, five verses selected from the Holy Quran. This done as a small number of verses contains all the Arabic phonemes. One hundred recitations of each verse were prepared in wave files. Verses had been recited by famous certified readers of the Holy Quran. A file created from each recite by extracting only the first word of the verse. A wave file of noise is set by the researcher's voice, for testing purpose. All wave files recorded at Sampling rate =22.050 kHz , PCM signed 16 bit mono. Three types of coefficients are extracted from each wave file to represent features of speech. They are Mel Frequency Cepstrum Coefficients MFCC, Power Spectral Density PSD and Reflection Coefficients RC. Also three techniques of speech recognition are used. They are Hidden Markov Models, Dynamic time warping and Artificial Neural Networks. Test were done at two levels. The first stage was applied at the full verse level. All the three techniques mentioned were used. HMMs and ANNs trained by the first 30 samples and test done by all the 100 sample of each verse. The second stage was applied in the same way, but the used samples were of the first word of the verse. HMMs technique only was selected to be used in recognition. That due to high recognition rates scored at the first stage. This stage repeated in the same way , but used the first 50 samples in training instead of the first 30 samples. Mainly, HMMs scored high rates of recognition to coefficients used(MFCC, PSD and RC). Low recognition rates with high confusion scored by ANNs and DTW at IIIverse level. For all coefficients used, high scores of recognition rates with low confusion rate concentrated in HMMs with MFCC. MFCC scored higher recognition rates than PSD and RC.