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arXiv · 2610.11099

Cross-Lingual Speaker Verification with Self-Supervised Pre-Trained Models

Jinghan Peng·Yu Zheng·Weiqiang Wang·Jian Liu
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Abstract

Speaker verification (SV) performance degrades under language mismatch due to the entanglement of speaker identity with language-specific acoustic cues. To address this problem, we leverage large-scale self-supervised pre-trained models (PTMs) to learn language-agnostic speaker representations. We utilize PTMs as robust front-end feature extractors, capitalizing on their rich acoustic and linguistic knowledge acquired from vast, diverse audio data. These generalized features are then used to train a downstream speaker embedding network, effectively disentangling speaker identity from language-specific characteristics. We validate our approach on the TidyVoice2026 benchmark, which benchmarks SV under language mismatch. Our proposed system (team T02) achieves equal error rates (EERs) of 2.21% on tv26_eval-A and 2.99% on tv26_eval-U.

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Jinghan Peng, Yu Zheng, Weiqiang Wang, Jian Liu. 2026-10-08. Cross-Lingual Speaker Verification with Self-Supervised Pre-Trained Models. https://doi.org/10.21437/interspeech.2026-1799

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