الفهرس 
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Abstract
Voiceprint identification is an important biometrically based technology for personal identification and verification. The motivation for endeavor stems from the fact that each person has a unique voiceprint pattern, which is formally accepted as a valid personal identification method by lawenforcement agencies. The automation of each system has several facets. In this thesis, the techniques of speech signal preprocessing, feature extraction, and neural networks are combined in order to analyze, identify and classify speech signal. Computer programs are developed to perform the analysis and classification of speech signal. The used speech database consists of 240 utterances; 10 repetitions of the same word, spoken by 4 different speakers (2 males, and 2 females). They spoke the Arabic numbers (1 , 2 , 3 , 4 , 5 , 6 )The used sampling rate was 16,000 sample/sec. The application of appropriate transforms in feature extraction stage (Fast Fourier Transform (FFT), Discrete Hartley Transform (DHT), Discrete Cosine Transform (DCT), Discrete WalshHadamard Transform (DWHT), Haar Transform (HT), Discrete Wavelet Transform (DWT) enabled important information from the speech signal to be identified: it<U+2019>s asserted that the use of wavelet transform gives the best identification ratio (98.75%). The results could possibly be improved further by increasing the database and the use of different classification methods (different neural topology).