  Most state-of-the-art Deep Learning (DL) approaches for speaker recognition
work on a short utterance level. Given the speech signal, these algorithms
extract a sequence of speaker embeddings from short segments and those are
averaged to obtain an utterance level speaker representation. In this work we
propose the use of an attention mechanism to obtain a discriminative speaker
embedding given non fixed length speech utterances. Our system is based on a
Convolutional Neural Network (CNN) that encodes short-term speaker features
from the spectrogram and a self multi-head attention model that maps these
representations into a long-term speaker embedding. The attention model that we
propose produces multiple alignments from different subsegments of the CNN
encoded states over the sequence. Hence this mechanism works as a pooling layer
which decides the most discriminative features over the sequence to obtain an
utterance level representation. We have tested this approach for the
verification task for the VoxCeleb1 dataset. The results show that self
multi-head attention outperforms both temporal and statistical pooling methods
with a 18\% of relative EER. Obtained results show a 58\% relative improvement
in EER compared to i-vector+PLDA.
