  Despite the growing popularity of metric learning approaches, very little
work has attempted to perform a fair comparison of these techniques for speaker
verification. We try to fill this gap and compare several metric learning loss
functions in a systematic manner on the VoxCeleb dataset. The first family of
loss functions is derived from the cross entropy loss (usually used for
supervised classification) and includes the congenerous cosine loss, the
additive angular margin loss, and the center loss. The second family of loss
functions focuses on the similarity between training samples and includes the
contrastive loss and the triplet loss. We show that the additive angular margin
loss function outperforms all other loss functions in the study, while learning
more robust representations. Based on a combination of SincNet trainable
features and the x-vector architecture, the network used in this paper brings
us a step closer to a really-end-to-end speaker verification system, when
combined with the additive angular margin loss, while still being competitive
with the x-vector baseline. In the spirit of reproducible research, we also
release open source Python code for reproducing our results, and share
pretrained PyTorch models on torch.hub that can be used either directly or
after fine-tuning.
