  In this paper we present an efficient method for training models for speaker
recognition using small or under-resourced datasets. This method requires less
data than other SOTA (State-Of-The-Art) methods, e.g. the Angular Prototypical
and GE2E loss functions, while achieving similar results to those methods. This
is done using the knowledge of the reconstruction of a phoneme in the speaker's
voice. For this purpose, a new dataset was built, composed of 40 male speakers,
who read sentences in Portuguese, totaling approximately 3h. We compare the
three best architectures trained using our method to select the best one, which
is the one with a shallow architecture. Then, we compared this model with the
SOTA method for the speaker recognition task: the Fast ResNet-34 trained with
approximately 2,000 hours, using the loss functions Angular Prototypical and
GE2E. Three experiments were carried out with datasets in different languages.
Among these three experiments, our model achieved the second best result in two
experiments and the best result in one of them. This highlights the importance
of our method, which proved to be a great competitor to SOTA speaker
recognition models, with 500x less data and a simpler approach.
