# Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages

arXiv: https://arxiv.org/abs/2607.01161 · HTML: https://mlsanity.com/paper/2607.01161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [Cross-modal Speaker Verification and Recognition: A Multilingual Perspective](https://mlsanity.com/paper/2004.13780/) (2020)
- [Cross-lingual Speaker Verification with Deep Feature Learning](https://mlsanity.com/paper/1706.07861/) (2017)
- [Tackling the Score Shift in Cross-Lingual Speaker Verification by Exploiting Language Information](https://mlsanity.com/paper/2110.09150/) (2021)
- [Disentangled representation learning for multilingual speaker recognition](https://mlsanity.com/paper/2211.00437/) (2022)
- [LASPA: Language Agnostic Speaker Disentanglement with Prefix-Tuned Cross-Attention](https://mlsanity.com/paper/2506.02083/) (2025)
- [On the influence of language similarity in non-target speaker verification trials](https://mlsanity.com/paper/2506.02777/) (2025)
- [Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks](https://mlsanity.com/paper/2603.08231/) (2026)

## Abstract (arXiv)

Cross-lingual speaker verification (SV) systems typically exhibit performance degradation when enrollment and test utterances are spoken in different languages. However, standard evaluation protocols confound language mismatch with inter-speaker variability, as evaluation is generally performed with different speakers across languages. In this work, we introduce a bilingual same-speaker evaluation set for five Iberian languages, enabling analysis of cross-lingual SV under constant speaker identity. We apply this setup to a HuBERT-based SV system previously shown to exhibit strong language dependence, and analyze results using the Cross-Lingual Transfer Matrix (CLTM) to study pairwise cross-lingual transfer. Our results show that speaker-related variability accounts for part of the observed degradation, but language mismatch remains the main driver of cross-lingual performance loss. These findings provide a more precise characterization of language dependence in cross-lingual SV.