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