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This technical report describes the IDLab submission for track 1 and 2 of theVoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). This speakerverification competition focuses on short duration test recordings andcross-lingual trials. Currently, both Time Delay Neural Networks (TDNNs) andResNets achieve state-of-the-art results in speaker verification. We opt to usea system fusion of hybrid architectures in our final submission. An ECAPA-TDNNbaseline is enhanced with a 2D convolutional stem to transfer some of thestrong characteristics of a ResNet based model to this hybrid CNN-TDNNarchitecture. Similarly, we incorporate absolute frequency positionalinformation in the SE-ResNet architectures. All models are trained with aspecial mini-batch data sampling technique which constructs mini-batches withdata that is the most challenging for the system on the level of intra-speakervariability. This intra-speaker variability is mainly caused by differences inlanguage and background conditions between the speaker's utterances. Thecross-lingual effects on the speaker verification scores are furthercompensated by introducing a binary cross-linguality trial feature in thelogistic regression based system calibration. The final system fusion with twoECAPA CNN-TDNNs and three SE-ResNets enhanced with frequency positionalinformation achieved a third place on the VoxSRC-21 leaderboard for both track1 and 2 with a minDCF of 0.1291 and 0.1313 respectively.

The IDLAB VoxCeleb Speaker Recognition Challenge 2021 System Description