A speaker identification system using a model of artificial neural networks for an elevator application

This paper presents a comparison of some features for speaker identification applied to a building security system. The features used in this paper are pitch, frequency formants, linear predictive coding (LPC) coefficients and cepstral coefficients computed from LPC. The comparison was based on a system for building security that uses the voice of the residents to control the access to the building. The system uses a model of artificial neural network called multi-layer perceptron (MLP) as a classifier. This paper shows that cepstral coefficients are more efficient than LPC coefficients for the security system.