# Leveraging Mixture of Experts for Improved Speech Deepfake Detection

arXiv: https://arxiv.org/abs/2409.16077 · HTML: https://mlsanity.com/paper/2409.16077/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts.
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [A Survey on Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2407.06204/) (2024)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [ADD 2022: the First Audio Deep Synthesis Detection Challenge](https://mlsanity.com/paper/2202.08433/) (2022)
- [A robust audio deepfake detection system via multi-view feature](https://mlsanity.com/paper/2403.01960/) (2024)
- [TIMIT-TTS: a Text-to-Speech Dataset for Multimodal Synthetic Media Detection](https://mlsanity.com/paper/2209.08000/) (2022)
- [FairSSD: Understanding Bias in Synthetic Speech Detectors](https://mlsanity.com/paper/2404.10989/) (2024)
- [Open Challenges in Synthetic Speech Detection](https://mlsanity.com/paper/2209.07180/) (2022)
- [All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection](https://mlsanity.com/paper/2307.15555/) (2023)

## Built on by (most cited first, 15 of 15)

- [ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference](https://mlsanity.com/paper/2510.26730/) (2025)
- [Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching](https://mlsanity.com/paper/2503.18032/) (2025)
- [Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection](https://mlsanity.com/paper/2509.13878/) (2025)
- [Attention-based Mixture of Experts for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.17585/) (2025)
- [End-to-end Audio Deepfake Detection from RAW Waveforms: a RawNet-Based Approach with Cross-Dataset Evaluation](https://mlsanity.com/paper/2504.20923/) (2025)
- [YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection](https://mlsanity.com/paper/2511.13344/) (2025)
- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System](https://mlsanity.com/paper/2508.20983/) (2025)
- [MME: Mixture of Mesh Experts with Random Walk Transformer Gating](https://mlsanity.com/paper/2603.00828/) (2026)
- [A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection](https://mlsanity.com/paper/2603.01482/) (2026)
- [From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing](https://mlsanity.com/paper/2606.14639/) (2026)
- [Traceback Translators Against Forgetting in Continual Fake Speech Detection](https://mlsanity.com/paper/2607.12569/) (2026)
- [Explainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Prediction](https://mlsanity.com/paper/2607.12584/) (2026)
- [Domain-Adaptive Dual-Gating Mixture of Experts for Generalizable Speech Deepfake Detection](https://mlsanity.com/paper/2609.33709/) (2026)
- [Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection](https://mlsanity.com/paper/2610.16049/) (2026)

## Abstract (arXiv)

Speech deepfakes pose a significant threat to personal security and content authenticity. Several detectors have been proposed in the literature, and one of the primary challenges these systems have to face is the generalization over unseen data to identify fake signals across a wide range of datasets. In this paper, we introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture. The Mixture of Experts framework is well-suited for the speech deepfake detection task due to its ability to specialize in different input types and handle data variability efficiently. This approach offers superior generalization and adaptability to unseen data compared to traditional single models or ensemble methods. Additionally, its modular structure supports scalable updates, making it more flexible in managing the evolving complexity of deepfake techniques while maintaining high detection accuracy. We propose an efficient, lightweight gating mechanism to dynamically assign expert weights for each input, optimizing detection performance. Experimental results across multiple datasets demonstrate the effectiveness and potential of our proposed approach.