| Popis: |
Arabic language is a semantic language that has complicated difficulties when compared to English and other languages. In this paper an Arabic speaker recognition system has been developed for introducing conversion of the uttered Arabic speaker instantly after the utterance. The voice samples were recorded, the pre-processing activity detected to evaluate the voice parts from unvoiced, framing and rectangular window slides techniques has been used for segmentation of the Arabic Speech signals, followed by Mel Frequency Spectrum Coefficients (MFCC) for features extractions, The feature vectors are grouped for each spoken sample using VQLBG Algorithm and Gaussian Mixer Model (GMM) applied for classification and recognition an unknowing speaker through his uttered words which belong to specific cluster that is differenced form others clusters related to others Arabic speakers. This approach reported in providing 95.5% of recognition rate. |
