Speaker recognition with a self-configuring neural network

This paper discusses preliminary work on a promising method for recognizing speakers. A self-configuring neural network is trained to recognize sentences that have been compressed by the LBG clustering algorithm. The bias weights of the trained neural networks are adjusted to minimize the false positive percentage. Recognition results from the TIMIT speech database of greater than 90% correct are obtained with no false positives. The results presented here provide a basis for the generation of secure speaker recognition systems which use neural networks