Text independent speaker recognition using LBG vector quantization

There is a great need for a system that will, in the absence of other biometric data, be able to identify the person by voice. This paper describes a system based on LBG vector quantization and the k-NN classifier, while the features that were used are MFCC coefficients and energy of the sound signal. Based on the described approach the developed system was evaluated on two sets of speakers. The results obtained are encouraging, with an accuracy of more than 95%. The system was also evaluated for the case of interference in the voice signal transmission, and accuracy in this case ranges from 70% up to 85%.