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

arXiv: https://arxiv.org/abs/2610.11099 · HTML: https://mlsanity.com/paper/2610.11099/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 29 of 29)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [SeamlessM4T: Massively Multilingual & Multimodal Machine Translation](https://mlsanity.com/paper/2308.11596/) (2023)
- [Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification](https://mlsanity.com/paper/2110.05777/) (2021)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training](https://mlsanity.com/paper/2110.05752/) (2021)
- [Large Margin Softmax Loss for Speaker Verification](https://mlsanity.com/paper/1904.03479/) (2019)
- [Reshape Dimensions Network for Speaker Recognition](https://mlsanity.com/paper/2407.18223/) (2024)
- [VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark](https://mlsanity.com/paper/2407.11510/) (2024)
- [3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement](https://mlsanity.com/paper/2306.15354/) (2023)
- [Exploring Binary Classification Loss For Speaker Verification](https://mlsanity.com/paper/2307.08205/) (2023)
- [TidyVoice: A Curated Multilingual Dataset for Speaker Verification Derived from Common Voice](https://mlsanity.com/paper/2601.16358/) (2026)
- [Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning](https://mlsanity.com/paper/2510.04213/) (2025)
- [The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.08766/) (2023)
- [TidyVoice 2026 Challenge Evaluation Plan](https://mlsanity.com/paper/2601.21960/) (2026)
- [Whisper-PMFA: Partial Multi-Scale Feature Aggregation for Speaker Verification using Whisper Models](https://mlsanity.com/paper/2408.15585/) (2024)
- [Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis](https://mlsanity.com/paper/2506.00733/) (2025)

## Abstract (arXiv)

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.