without someone so the world that i'm going to present so you today is and
extend personal for one of the sub subsystems so that the ice quality man
submitted form needs the elderly and the false and fifteen challenge
and the
although it is focused on delivery a fifteen we believe that the key components or
four
in this of this paper they can be used in a much wider context
so the first thing that we explore is that the how to more get the
most of the key lda for
and discriminant for discriminative training
because nist ovaries that close the set identification task and it's going to train it
should be the best
so the most important thing is that we show that if we if we're
and i u
if we used plp parameters to projects i-vectors on the p lda latent subspace apply
it discriminative methods in that subspace and then project them but
then a week and improve the performance compared to just the baseline the one
we use lda and then
and maximum mutual information of their from on top of it
and the second the
important thing is that the we show how four
take
lre
cost function
approximated and use it as an objective function for a discriminative l the difference
so that as a can see
it is based on false acceptance rate and false rejection rate and all of them
use indicator functions so we approximate those functions
into continues functions and then
where able to differentiate them
and the of course of these method in general you can be used for any
in a cost function in theory
okay thank you for attention to the