Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large Margin Softmax Loss for Speaker Verification   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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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th