# Tackling the Score Shift in Cross-Lingual Speaker Verification by Exploiting Language Information

arXiv: https://arxiv.org/abs/2110.09150 · HTML: https://mlsanity.com/paper/2110.09150/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2021.

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

- [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023)
- [A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures](https://mlsanity.com/paper/2306.00634/) (2023)
- [POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan](https://mlsanity.com/paper/2603.24569/) (2026)
- [Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification](https://mlsanity.com/paper/2409.15974/) (2024)
- [L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification](https://mlsanity.com/paper/2606.17416/) (2026)
- [Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages](https://mlsanity.com/paper/2607.01161/) (2026)

## Abstract (arXiv)

This paper contains a post-challenge performance analysis on cross-lingual speaker verification of the IDLab submission to the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). We show that current speaker embedding extractors consistently underestimate speaker similarity in within-speaker cross-lingual trials. Consequently, the typical training and scoring protocols do not put enough emphasis on the compensation of intra-speaker language variability. We propose two techniques to increase cross-lingual speaker verification robustness. First, we enhance our previously proposed Large-Margin Fine-Tuning (LM-FT) training stage with a mini-batch sampling strategy which increases the amount of intra-speaker cross-lingual samples within the mini-batch. Second, we incorporate language information in the logistic regression calibration stage. We integrate quality metrics based on soft and hard decisions of a VoxLingua107 language identification model. The proposed techniques result in a 11.7% relative improvement over the baseline model on the VoxSRC-21 test set and contributed to our third place finish in the corresponding challenge.