Neural network based speaker verification for security systems

This paper investigates the use of neural networks and MFCC coefficients for automatic, text-dependent, speaker verification in security systems. We have aimed to optimize the classification performance in terms of learning strategy and neural network architecture, as well as to establish the optimal choice of parameters for the voice signature, where MFCC coefficients derivatives are considered. The performance evaluation is conducted on a database of 600 waveforms of a Serbo-Croatian utterance “lozinka” (meaning “password”).