Abstract
While multilayer perceptrons (MLPs) have great possibility on the application to speaker verification, they suffer from an inferior learning speed. To appeal to users, the speaker verification systems based on MLPs must achieve a reasonable speed of user enrolling and it is thoroughly dependent on fast learning of MLPs. To attain real-time enrollment for the systems, the previous two studies, the discriminative cohort speakers (DCS) method and the omitting patterns in instant learning (OIL) method, have been devoted to the problem and each satisfied that objective. In this paper, we combine the two methods and apply the combination to the systems, assuming that the two methods operate on different optimization principles. Through experiment on real speech database using an MLP-based speaker verification system to which the combination is applied, the feasibility of the combination is verified from the results. Keywords: Biometric authentication system, speaker verification, multiplayer perceptrons, error backpropagation, real-time enrollment, discriminative cohort speakers, omitting patterns in instant learning
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Lee, TS., Hwang, BW. (2004). Optimization of Usability on an Authentication System Built from Voice and Neural Networks. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24767-8_40
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DOI: https://doi.org/10.1007/978-3-540-24767-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22057-2
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