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A new mixture of experts framework for acoustic modelling using SOM clustering and radial basis functions

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

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Abstract

In machine learning applications, Supervised Mixture models and Mixture of experts play vital role in performing pattern classification or phoneme classification eg. acoustic modelling of speech recognition. In this paper, we introduce a new mixture of experts' classification kernel by embedding self organized map (SOM) clustering with mixture of radial basis function (RBF) networks. The model's efficacy is demonstrated in solving a multi-class TIMIT speech recognition problem where the kernel is used to learn the multidimensional cepstral feature vectors to estimate their posterior class probabilities. The tests results have shown that this model provides a better alternative to the state of the art models achieving a significant improvement in error performance, reduction in complexity and gain in training time.


Publication metadata

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

Publication type: Article

Publication status: Published

Journal: WSEAS Transactions on Computers

Year: 2005

Volume: 4

Issue: 12

Pages: 1733-1740

ISSN (print): 1109-2750

ISSN (electronic):

Publisher: World Scientific and Engineering Academy and Society


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