  The state-of-art approach to speaker verification involves the extraction of
discriminative embeddings like x-vectors followed by a generative model
back-end using a probabilistic linear discriminant analysis (PLDA). In this
paper, we propose a Pairwise neural discriminative model for the task of
speaker verification which operates on a pair of speaker embeddings such as
x-vectors/i-vectors and outputs a score that can be considered as a scaled
log-likelihood ratio. We construct a differentiable cost function which
approximates speaker verification loss, namely the minimum detection cost. The
pre-processing steps of linear discriminant analysis (LDA), unit length
normalization and within class covariance normalization are all modeled as
layers of a neural model and the speaker verification cost functions can be
back-propagated through these layers during training. We also explore
regularization techniques to prevent overfitting, which is a major concern in
using discriminative back-end models for verification tasks. The experiments
are performed on the NIST SRE 2018 development and evaluation datasets. We
observe average relative improvements of 8% in CMN2 condition and 30% in VAST
condition over the PLDA baseline system.
