SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION

Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified a subject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformation as a feature extraction method and an artificial neural network of back-propagation as a classification method. The voice input was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as a decomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural network of back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in non soundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as a training voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done on the subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has no influence on the accuracy rate produced by system.