MLP trained to separate problem speakers provides improved features for speaker identification

Abstract

In automatic speech recognition (ASR) the non-linear data projection provided by a one hidden layer multilayer perceptron (MLP), trained to recognise phonemes, has previously been shown to provide feature enhancement which can substantially increase ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.