High performance speaker verification system based on multilayer perceptrons and real-time enrollment

Abstract

Speaker verification systems based on multilayer perceptrons (MLI's) have good prospects in reliability and flexibility as required for a successful authentication system. However, poor learning speed of error backpropagation (EBP), the representative method of learning for MLPs, has been the major problem which must be resolved to achieve real-time user enrollment. In this paper, we implement an MLP-based speaker verification system by applying methods of omitting patterns in instant learning (OIL) and discriminative cohort speakers (DCS) to approach the real-time enrollment. We evaluate the system on a Korean speech database and demonstrate the feasibility of it as a speaker verification system of high performance.