  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.
