Learning statistically efficient features for speaker recognition

Abslracl-In this paper, a new architecture of divide-andconquer based radial basis function network (DCRBF) and its learning algorithm are presented. The DCRBF network is a :hybrid system consisting of several sub-RBF networks, each of which individually takes a sub-input space as its input. The output of this new. architecture is a linear combination of the sub-networks' outputs with the coefficients tuned together with each snb-netwnrk system parameters. Since this system divides a high-dimensional modelling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning speed as a whole is significantly improved with the comparable generalization ability. We apply DCRBF to model a recurrent version of RBF networks. The experimental results have shown its outstanding performance.

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Learning statistically efficient features for speaker recognition

Cited by 7 publications

References 6 publications

A Comparative Study of Linear and Nonlinear Dimensionality Reduction for Speaker Identification

A Comparative Study of Linear and Nonlinear Dimensionality Reduction for Speaker Identification

Text Independent Speaker Verification Based on Mixing ICA Overcomplete Basis Functions

A divide-and-conquer based radial basis function network with application to recurrent modelling

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