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Large Margin Softmax Loss for Speaker Verification

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arxiv 1904.03479 v1 pith:PI6GJIHP submitted 2019-04-06 cs.SD eess.AS

Yi Liu , Liang He , Jia Liu This is my paper · ORCID
classification cs.SDeess.AS
keywords lossspeakerverificationlargemarginsoftmaxachieveapproach
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In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small. A variety of loss functions have been proposed to achieve this goal. In this paper, we investigate the large margin softmax loss with different configurations in speaker verification. Ring loss and minimum hyperspherical energy criterion are introduced to further improve the performance. Results on VoxCeleb show that our best system outperforms the baseline approach by 15\% in EER, and by 13\%, 33\% in minDCF08 and minDCF10, respectively.

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  1. A Study on Angular Based Embedding Learning for Text-independent Speaker Verification

    cs.LG 2019-08 conditional novelty 5.0 of 10

    Angular margin losses with an inter-class regularization reduce speaker verification EER from 5.33% to 4.45% on a VoxCeleb test set compared with a softmax baseline.

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