---
title: "Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.01161v1"
permalink: "https://feed.astralor.com/items/itm_32d82785f021abd869739dcd"
published: "2026-07-01T16:44:19.000Z"
score: 54
channel: "ai"
tags: ["HuBERT","Iberian Languages","Speaker Verification","Cross-lingual"]
---

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

## At a glance

Cross-lingual speaker verification (SV) systems typically degrade when enrollment and test utterances are in different languages, but standard protocols confound language mismatch with inter-speaker variability. This work introduces a bilingual same-speaker evaluation set for five Iberian languages, enabling analysis under constant speaker identity. Using a HuBERT-based SV system and the Cross-Lingual Transfer Matrix (CLTM), results show speaker variability accounts for part of the degradation, but language mismatch remains the primary driver. These findings provide a more precise characterization of language dependence in cross-lingual SV.

## Key points

- Introduces a bilingual same-speaker evaluation set for five Iberian languages, isolating speaker from language effects.
- Employs a HuBERT-based SV system and the CLTM to quantify pairwise cross-lingual transfer.
- Finds language mismatch is the primary driver of cross-lingual performance loss, with speaker variability explaining only part of the degradation.

## Full text

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.

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