Title: Tied Hidden Factors in Neural Networks for End-to-End Speaker Recognition,
Abstract: In this paper we propose a method to model speaker and session variability
and able to generate likelihood ratios using neural networks in an end-to-end
phrase dependent speaker verification system. As in Joint Factor Analysis, the
model uses tied hidden variables to model speaker and session variability and a
MAP adaptation of some of the parameters of the model. In the training
procedure our method jointly estimates the network parameters and the values of
the speaker and channel hidden variables. This is done in a two-step
backpropagation algorithm, first the network weights and factor loading
matrices are updated and then the hidden variables, whose gradients are
calculated by aggregating the corresponding speaker or session frames, since
these hidden variables are tied. The last layer of the network is defined as a
linear regression probabilistic model whose inputs are the previous layer
outputs. This choice has the advantage that it produces likelihoods and
additionally it can be adapted during the enrolment using MAP without the need
of a gradient optimization. The decisions are made based on the ratio of the
output likelihoods of two neural network models, speaker adapted and universal
background model. The method was evaluated on the RSR2015 database.