Speaker verification: minimizing the channel effects using autoassociative neural network models

Abstract

The characteristics of the telephone channel and handset have a significant effect on the performance of speaker verification systems. The channel/handset mismatch between the training and testing data degrades the performance of speaker verification systems. In this paper, we show that the autoassociative neural network (AANN) models can be used to minimize the effects of channel characteristics on the performance of a text-independent speaker verification system. This paper also compares two approaches to represent the background model for an AANN based speaker verification system.