  Learning a good speaker embedding is important for many automatic speaker
recognition tasks, including verification, identification and diarization. The
embeddings learned by softmax are not discriminative enough for open-set
verification tasks. Angular based embedding learning target can achieve such
discriminativeness by optimizing angular distance and adding margin penalty. We
apply several different popular angular margin embedding learning strategies in
this work and explicitly compare their performance on Voxceleb speaker
recognition dataset. Observing the fact that encouraging inter-class
separability is important when applying angular based embedding learning, we
propose an exclusive inter-class regularization as a complement for angular
based loss. We verify the effectiveness of these methods for learning a
discriminative embedding space on ASV task with several experiments. These
methods together, we manage to achieve an impressive result with 16.5%
improvement on equal error rate (EER) and 18.2% improvement on minimum
detection cost function comparing with baseline softmax systems.
