Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58950
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Type: Journal article
Title: Adaptive speech recognition with evolving connectionist systems
Author: Ghobakhlou, Akbar
Watts, Michael John
Kasabov, Nikola K.
Citation: Information sciences, 2003; 156(1-2):71-83
Publisher: Elsevier
Issue Date: 2010
ISSN: 0020-0255
School/Discipline: School of Earth and Environmental Sciences
Statement of
Responsibility: 
Akbar Ghobakhlou, Michael Watts and Nikola Kasabov
Abstract: The paper presents a novel approach towards building adaptive speech recognition systems based on the evolving connectionist systems paradigm (ECoS). The simple evolving connectionist systems are the minimalist implementation of the ECoS. They can accommodate new input data and new classes through local element tuning. New connections and neurons are created during the adaptive learning process of the system. Experiments are conducted to illustrate this concept. It is demonstrated that a system can adapt to new speakers data and add new output classes on-line, e.g. new words, added at any time of its operation without having to rebuild the network from “scratch”. The system is robust to forgetting when new words are added.
Keywords: Evolving connectionist systems (ECoS); Simple evolving connectionist system (SECoS); Adaptive speech recognition; Isolated word recognition
Rights: Copyright © 2003 Elsevier B.V. All rights reserved.
DOI: 10.1016/S0020-0255(03)00165-8
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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