# Disentangled representation learning for multilingual speaker recognition

arXiv: https://arxiv.org/abs/2211.00437 · HTML: https://mlsanity.com/paper/2211.00437/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [LASPA: Language Agnostic Speaker Disentanglement with Prefix-Tuned Cross-Attention](https://mlsanity.com/paper/2506.02083/) (2025)
- [CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generation](https://mlsanity.com/paper/2412.20048/) (2024)
- [SEED: Speaker Embedding Enhancement Diffusion Model](https://mlsanity.com/paper/2505.16798/) (2025)
- [Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages](https://mlsanity.com/paper/2607.01161/) (2026)

## Abstract (arXiv)

The goal of this paper is to learn robust speaker representation for bilingual speaking scenario. The majority of the world's population speak at least two languages; however, most speaker recognition systems fail to recognise the same speaker when speaking in different languages. Popular speaker recognition evaluation sets do not consider the bilingual scenario, making it difficult to analyse the effect of bilingual speakers on speaker recognition performance. In this paper, we publish a large-scale evaluation set named VoxCeleb1-B derived from VoxCeleb that considers bilingual scenarios. We introduce an effective disentanglement learning strategy that combines adversarial and metric learning-based methods. This approach addresses the bilingual situation by disentangling language-related information from speaker representation while ensuring stable speaker representation learning. Our language-disentangled learning method only uses language pseudo-labels without manual information.