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Committees of RBFN - A novel connectionist model for speech recognition

Lookup NU author(s): Srinivasan Meenakshi Sundaram, Dr Wai Lok Woo, Professor Satnam Dlay

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Abstract

In this paper we present a novel paradigm of Artificial Intelligent (AI) connectionist model namely committee machine architecture (CM) using Radial Basis Functions (RBFN) for speech recognition applications. The architecture and training scheme associated with the CM is presented. Then the performance evaluation based on the theory is given and the analysis results are verified for conformance. Importantly we have achieved 10 % improvement on word recognition rate over the best connectionist methods and an impressive 18.082 % over the baseline HMM. Critically the error rate is reduced by 10.61% over other connectionist models and 23.29 % over baseline HMM method.


Publication metadata

Author(s): Meenakshisundaram S, Woo WL, Dlay SS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 8th IASTED International Conference on Artificial Intelligence and Soft Computing

Year of Conference: 2004

Pages: 300-304

Publisher: IASTED

Library holdings: Search Newcastle University Library for this item

ISBN: 0889864586


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