# Noise-Conditioned Mixture-of-Experts Framework for Robust Speaker Verification

arXiv: https://arxiv.org/abs/2510.18533 · HTML: https://mlsanity.com/paper/2510.18533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Placed in

- Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/)

## Builds on (most cited first, 17 of 17)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [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)
- [A Survey on Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2407.06204/) (2024)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit](https://mlsanity.com/paper/2210.17016/) (2022)
- [VoiceID Loss: Speech Enhancement for Speaker Verification](https://mlsanity.com/paper/1904.03601/) (2019)
- [Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning](https://mlsanity.com/paper/2407.15188/) (2024)
- [Within-sample variability-invariant loss for robust speaker recognition under noisy environments](https://mlsanity.com/paper/2002.00924/) (2020)
- [Feature Enhancement with Deep Feature Losses for Speaker Verification](https://mlsanity.com/paper/1910.11905/) (2019)
- [Extended U-Net for Speaker Verification in Noisy Environments](https://mlsanity.com/paper/2206.13044/) (2022)
- [Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.08320/) (2023)
- [A Joint Noise Disentanglement and Adversarial Training Framework for Robust Speaker Verification](https://mlsanity.com/paper/2408.11562/) (2024)
- [ParaNoise-SV: Integrated Approach for Noise-Robust Speaker Verification with Parallel Joint Learning of Speech Enhancement and Noise Extraction](https://mlsanity.com/paper/2508.07219/) (2025)

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

- [Modular Foundation Models for Time-Series Perception in Digital Twins](https://mlsanity.com/paper/2607.03585/) (2026)

## Abstract (arXiv)

Robust speaker verification under noisy conditions remains an open challenge. Conventional deep learning methods learn a robust unified speaker representation space against diverse background noise and achieve significant improvement. In contrast, this paper presents a noise-conditioned mixture-ofexperts framework that decomposes the feature space into specialized noise-aware subspaces for speaker verification. Specifically, we propose a noise-conditioned expert routing mechanism, a universal model based expert specialization strategy, and an SNR-decaying curriculum learning protocol, collectively improving model robustness and generalization under diverse noise conditions. The proposed method can automatically route inputs to expert networks based on noise information derived from the inputs, where each expert targets distinct noise characteristics while preserving speaker identity information. Comprehensive experiments demonstrate consistent superiority over baselines