  In recent years, speaker verification has primarily performed using deep
neural networks that are trained to output embeddings from input features such
as spectrograms or Mel-filterbank energies. Studies that design various loss
functions, including metric learning have been widely explored. In this study,
we propose two end-to-end loss functions for speaker verification using the
concept of speaker bases, which are trainable parameters. One loss function is
designed to further increase the inter-speaker variation, and the other is
designed to conduct the identical concept with hard negative mining. Each
speaker basis is designed to represent the corresponding speaker in the process
of training deep neural networks. In contrast to the conventional loss
functions that can consider only a limited number of speakers included in a
mini-batch, the proposed loss functions can consider all the speakers in the
training set regardless of the mini-batch composition. In particular, the
proposed loss functions enable hard negative mining and calculations of
between-speaker variations with consideration of all speakers. Through
experiments on VoxCeleb1 and VoxCeleb2 datasets, we confirmed that the proposed
loss functions could supplement conventional softmax and center loss functions.
