Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58948
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Type: Journal article
Title: Phoneme-based speech recognition via fuzzy neural networks modeling and learning
Author: Kasabov, Nikola K.
Kozma, R.
Watts, Michael John
Citation: Information Sciences, 1998; 110(1-2):61-79
Publisher: Elsevier
Issue Date: 1998
School/Discipline: School of Earth and Environmental Sciences
Statement of
Responsibility: 
Kasabov, N.K. and Kozma, R. and Watts, M. J.
Abstract: Fuzzy neural networks (FNN) have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consideration. The paper presents one particular architecture called FuNN and discusses two alternative ways to optimise its structure, namely a genetic algorithm and a method of learning-with-forgetting. The optimised structure has much less connections and can easily be interpreted in terms of fuzzy rules. Such a structure can be effectively used for on-line adaptation which is demonstrated on a phoneme-based speech recognition problem.
Keywords: Fuzzy neural network; Speech recognition; Structural learning
Rights: © 1998 Elsevier Science Inc. All rights reserved.
DOI: 10.1016/S0020-0255(97)10077-9
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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