Speakers Recognition Based On Convolution

In this research, the important features of recognizing speakers' identity by extracting features of speakers voices are used. Voice is considered one of the vital factors adopted algorithm on samples of speakers' voices has been implemented by recording the voice, saving it on a file of the type wav. The voice is treated by using Hamming function to reduce error ratio. Feature of the voice samples have been extracted by taking the spectrum value of the sound signal of all the speakers. Two methods have been used (Ecledian distance and correlation factor) for comparing core of the convolution of the sample of speaker test (a training sample which is being recorded another time) and all the speakers samples who have already been saved on the data base. Applying the algorithm on different voices, ratio of error was very small while matching has increased by the increasing number of speaker. The algorithm has been implemented using mathlab.