  This paper explores two techniques to improve the performance of
text-dependent speaker verification systems based on deep neural networks.
Firstly, we propose a general alignment mechanism to keep the temporal
structure of each phrase and obtain a supervector with the speaker and phrase
information, since both are relevant for a text-dependent verification. As we
show, it is possible to use different alignment techniques to replace the
global average pooling providing significant gains in performance. Moreover, we
also present a novel back-end approach to train a neural network for detection
tasks by optimizing the Area Under the Curve (AUC) as an alternative to the
usual triplet loss function, so the system is end-to-end, with a cost function
close to our desired measure of performance. As we can see in the experimental
section, this approach improves the system performance, since our triplet
neural network based on an approximation of the AUC (aAUC) learns how to
discriminate between pairs of examples from the same identity and pairs of
different identities. The different alignment techniques to produce
supervectors in addition to the new back-end approach were tested on the
RSR2015-Part I database for text-dependent speaker verification, providing
competitive results compared to similar size networks using the global average
pooling to extract supervectors and using a simple back-end or triplet loss
training.
