  Open-set speaker recognition can be regarded as a metric learning problem,
which is to maximize inter-class variance and minimize intra-class variance.
Supervised metric learning can be categorized into entity-based learning and
proxy-based learning. Most of the existing metric learning objectives like
Contrastive, Triplet, Prototypical, GE2E, etc all belong to the former
division, the performance of which is either highly dependent on sample mining
strategy or restricted by insufficient label information in the mini-batch.
Proxy-based losses mitigate both shortcomings, however, fine-grained
connections among entities are either not or indirectly leveraged. This paper
proposes a Masked Proxy (MP) loss which directly incorporates both proxy-based
relationships and pair-based relationships. We further propose Multinomial
Masked Proxy (MMP) loss to leverage the hardness of speaker pairs. These
methods have been applied to evaluate on VoxCeleb test set and reach
state-of-the-art Equal Error Rate(EER).
