# The IDLAB VoxCeleb Speaker Recognition Challenge 2021 System Description

arXiv: https://arxiv.org/abs/2109.04070 · HTML: https://mlsanity.com/paper/2109.04070/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2021.

## Built on by (most cited first, 6 of 6)

- [Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech](https://mlsanity.com/paper/2208.05445/) (2022)
- [The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022](https://mlsanity.com/paper/2209.11625/) (2022)
- [CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization](https://mlsanity.com/paper/2110.13465/) (2021)
- [The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.10147/) (2022)
- [The Royalflush System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.09010/) (2022)
- [The SpeakIn System Description for CNSRC2022](https://mlsanity.com/paper/2209.10846/) (2022)

## Abstract (arXiv)

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