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This paper presents a novel approach to speaker subspace modelling based onGaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model isbased on the idea of shared factors as in the Probabilistic Linear DiscriminantAnalysis (PLDA). GRBM hidden layer is divided into speaker and channel factors,herein the speaker factor is shared over all vectors of the speaker. ThenMaximum Likelihood Parameter Estimation (MLE) for proposed model is introduced.Various new scoring techniques for speaker verification using GRBM areproposed. The results for NIST i-vector Challenge 2014 dataset are presented.

Shared latent subspace modelling within Gaussian-Binary Restricted Boltzmann Machines for NIST i-Vector Challenge 2014