  Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular
softmax loss with margin penalties toenhance the intra-class compactness of
speaker embeddings,which achieved remarkable performance. In this paper, we
pro-pose a novel angular loss function called adaptive margin cir-cle loss for
speaker verification. The stage-based margin andchunk-based margin are applied
to improve the angular discrim-ination of circle loss on the training set. The
analysis on gradi-ents shows that, compared with the previous angular loss
likeAdditive Margin Softmax(Am-Softmax), circle loss has flexi-ble optimization
and definite convergence status. Experimentsare carried out on the Voxceleb and
SITW. By applying adap-tive margin circle loss, our best system achieves
1.31%EER onVoxceleb1 and 2.13% on SITW core-core.
