  In this technical report we describe the IDLAB top-scoring submissions for
the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) in the supervised
and unsupervised speaker verification tracks. For the supervised verification
tracks we trained 6 state-of-the-art ECAPA-TDNN systems and 4 Resnet34 based
systems with architectural variations. On all models we apply a large margin
fine-tuning strategy, which enables the training procedure to use higher margin
penalties by using longer training utterances. In addition, we use
quality-aware score calibration which introduces quality metrics in the
calibration system to generate more consistent scores across varying levels of
utterance conditions. A fusion of all systems with both enhancements applied
led to the first place on the open and closed supervised verification tracks.
The unsupervised system is trained through contrastive learning. Subsequent
pseudo-label generation by iterative clustering of the training embeddings
allows the use of supervised techniques. This procedure led to the winning
submission on the unsupervised track, and its performance is closing in on
supervised training.
