# A Joint Noise Disentanglement and Adversarial Training Framework for Robust Speaker Verification

arXiv: https://arxiv.org/abs/2408.11562 · HTML: https://mlsanity.com/paper/2408.11562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Noise-Conditioned Mixture-of-Experts Framework for Robust Speaker Verification](https://mlsanity.com/paper/2510.18533/) (2025)

## Abstract (arXiv)

Automatic Speaker Verification (ASV) suffers from performance degradation in noisy conditions. To address this issue, we propose a novel adversarial learning framework that incorporates noise-disentanglement to establish a noise-independent speaker invariant embedding space. Specifically, the disentanglement module includes two encoders for separating speaker related and irrelevant information, respectively. The reconstruction module serves as a regularization term to constrain the noise. A feature-robust loss is also used to supervise the speaker encoder to learn noise-independent speaker embeddings without losing speaker information. In addition, adversarial training is introduced to discourage the speaker encoder from encoding acoustic condition information for achieving a speaker-invariant embedding space. Experiments on VoxCeleb1 indicate that the proposed method improves the performance of the speaker verification system under both clean and noisy conditions.