NEURAL NETWORKS FOR DISCRIMINATION AND MODELIZATION OF SPEAKERS

This article reviews current research on neural network systems for speaker recognition tasks. We consider two main approaches, the first one relies on direct classification and the second on speaker modelization. The potential of connectionist models for speaker recognition is first presented and the main models are briefly introduced. We then present different systems which have been recently proposed for speaker recognition tasks. We discuss their respective performances and potentials and compare these techniques to more conventional methods like vector quantization and Hidden Markov models. The paper ends with a summary and suggestions for further developments.