Speaker verification systems based on multilayer perceptrons (MLPs) 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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Lee, T. S., & Choi, H. J. (2004). High performance speaker verification system based on multilayer perceptrons and real-time enrollment. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3072, 623–630. https://doi.org/10.1007/978-3-540-25948-0_85
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