Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The IDLAB VoxSRC-20 Submission: Large Margin Fine-Tuning and
  Quality-Aware Score Calibration in DNN Based Speaker Verification   In this paper we propose and analyse a large margin fine-tuning strategy and
a quality-aware score calibration in text-independent speaker verification.
Large margin fine-tuning is a secondary training stage for DNN based speaker
verification systems trained with margin-based loss functions. It enables the
network to create more robust speaker embeddings by enabling the use of longer
training utterances in combination with a more aggressive margin penalty. Score
calibration is a common practice in speaker verification systems to map output
scores to well-calibrated log-likelihood-ratios, which can be converted to
interpretable probabilities. By including quality features in the calibration
system, the decision thresholds of the evaluation metrics become
quality-dependent and more consistent across varying trial conditions. Applying
both enhancements on the ECAPA-TDNN architecture leads to state-of-the-art
results on all publicly available VoxCeleb1 test sets and contributed to our
winning submissions in the supervised verification tracks of the VoxCeleb
Speaker Recognition Challenge 2020.

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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph