Adaptive Margin Circle Loss for Speaker Verification

Abstract

Deep-Neural-Network (DNN) based speaker verification systems use the angular softmax loss with margin penalties to enhance the intra-class compactness of speaker embeddings, which achieved remarkable performance. In this paper, we propose a novel angular loss function called adaptive margin circle loss for speaker verification. The stage-based margin and chunk-based margin are applied to improve the angular discrimination of circle loss on the training set. The analysis on gradients shows that, compared with the previous angular loss like Additive Margin Softmax(Am-Softmax), circle loss has flexible optimization and definite convergence status. Experiments are carried out on the Voxceleb and SITW. By applying adaptive margin circle loss, our best system achieves 1.31%EER on Voxceleb1 and 2.13% on SITW core-core.