This paper introduces an approach based on a generative model named Sparse Probabilistic Linear Discriminant Analysis in speaker verification. The model provides an alternative approach to deal with the non-Gaussian behavior of the latent variables, directly assuming they are based on Laplace prior. This distribution encourages the model to set many latent variables to zero. An expectation-maximization algorithm is derived to train model with a variational approximation to a range of heavy-tailed distributions whose limit is the Laplace. The variational approximation is also used to compute of likelihood ratio. This approach performed well on the tel-tel extended condition of the NIST 2010 Speaker Recognition Evaluation, and is competitive compared to the Gaussian Probabilistic Linear Discriminant Analysis, in terms of normalized Decision Cost Function and Equal Error Rate.

Index Terms: speaker verification, i-vectors, sparse, Laplace prior