# MoLEx: Mixture of LoRA Experts in Speech Self-Supervised Models for Audio Deepfake Detection

arXiv: https://arxiv.org/abs/2509.09175 · HTML: https://mlsanity.com/paper/2509.09175/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [Quantizer-Aware Hierarchical Neural Codec Modeling for Speech Deepfake Detection](https://mlsanity.com/paper/2603.16914/) (2026)
- [From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing](https://mlsanity.com/paper/2606.14639/) (2026)
- [Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detection](https://mlsanity.com/paper/2606.25328/) (2026)
- [Domain-Adaptive Dual-Gating Mixture of Experts for Generalizable Speech Deepfake Detection](https://mlsanity.com/paper/2609.33709/) (2026)

## Abstract (arXiv)

While self-supervised learning (SSL)-based models have boosted audio deepfake detection accuracy, fully finetuning them is computationally expensive. To address this, we propose a parameter-efficient framework that combines Low-Rank Adaptation with a Mixture-of-Experts router, called Mixture of LoRA Experts (MoLEx). It preserves pre-trained knowledge of SSL models while efficiently finetuning only selected experts, reducing training costs while maintaining robust performance. The observed utility of experts during inference shows the router reactivates the same experts for similar attacks but switches to other experts for novel spoofs, confirming MoLEx's domain-aware adaptability. MoLEx additionally offers flexibility for domain adaptation by allowing extra experts to be trained without modifying the entire model. We mainly evaluate our approach on the ASVSpoof 5 dataset and achieve the state-of-the-art (SOTA) equal error rate (EER) of 5.56% on the evaluation set without augmentation.