Genetically optimised feedforward neural networks for speaker identification

The problem of identifying a speaker from a given utterance has been conventionally addressed using techniques such as Gaussian mixture models (GMMs) that model the characteristics of a known speaker via means and covariances. We compare the performance of a genetically optimised neural network speaker identification system versus the conventional approach of GMMs. The test data used in the experiments was the data used for the 1996 National Institute for Standards Technology (NIST) evaluation of speaker identification systems.