Shared latent subspace modelling within Gaussian-Binary Restricted Boltzmann Machines for NIST i-Vector Challenge 2014
This work addresses speaker verification for security or biometric applications, but appears incremental as it builds on existing methods like PLDA and GRBMs.
The paper tackled speaker verification by modeling speaker and channel factors within Gaussian-Binary Restricted Boltzmann Machines, achieving results on the NIST i-vector Challenge 2014 dataset.
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.