  This paper introduces a semi-supervised contrastive learning framework and
its application to text-independent speaker verification. The proposed
framework employs generalized contrastive loss (GCL). GCL unifies losses from
two different learning frameworks, supervised metric learning and unsupervised
contrastive learning, and thus it naturally determines the loss for
semi-supervised learning. In experiments, we applied the proposed framework to
text-independent speaker verification on the VoxCeleb dataset. We demonstrate
that GCL enables the learning of speaker embeddings in three manners,
supervised learning, semi-supervised learning, and unsupervised learning,
without any changes in the definition of the loss function.
