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A new approach for isolated word recognition using dynamic synapse neural networks

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3 Author(s)
Dibazar, A.A. ; Dept. of Biomed. Eng., California State Univ., Los Angeles, CA, USA ; Narnarvar, H.H. ; Berger, T.W.

We focus on the development of an efficient method for estimating the parameters of continuous dynamic synapse neural networks (cDSNN). We implement higher order differential equations in the cDSNN, necessitating a minor adjustment to the cDSNN architecture. The estimation of network parameters is based on extension of the quasi-linearization algorithm, which provides an explicit analytic representation for the solution of a nonlinear differential equation. We use higher order cDSNNs trained with the extended quasilinearization algorithm to the isolated word recognition task. The features derived from cDSNNs are classified using a HMM based classifier. We show that cDSNN based features are more robust in the presence of additive Gaussian white noise than state of-the-art Mel frequency features.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003

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