# Cross-lingual Speaker Verification with Deep Feature Learning

arXiv: https://arxiv.org/abs/1706.07861 · HTML: https://mlsanity.com/paper/1706.07861/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing](https://mlsanity.com/paper/2409.08346/) (2024)
- [Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings](https://mlsanity.com/paper/2209.12602/) (2022)
- [Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks](https://mlsanity.com/paper/2603.08231/) (2026)
- [Multilingual Audio-Visual Smartphone Dataset And Evaluation](https://mlsanity.com/paper/2109.04138/) (2021)
- [Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials](https://mlsanity.com/paper/2404.01981/) (2024)
- [Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages](https://mlsanity.com/paper/2607.01161/) (2026)

## Abstract (arXiv)

Existing speaker verification (SV) systems often suffer from performance degradation if there is any language mismatch between model training, speaker enrollment, and test. A major cause of this degradation is that most existing SV methods rely on a probabilistic model to infer the speaker factor, so any significant change on the distribution of the speech signal will impact the inference. Recently, we proposed a deep learning model that can learn how to extract the speaker factor by a deep neural network (DNN). By this feature learning, an SV system can be constructed with a very simple back-end model. In this paper, we investigate the robustness of the feature-based SV system in situations with language mismatch. Our experiments were conducted on a complex cross-lingual scenario, where the model training was in English, and the enrollment and test were in Chinese or Uyghur. The experiments demonstrated that the feature-based system outperformed the i-vector system with a large margin, particularly with language mismatch between enrollment and test.