aiqbench analysis
This research addresses the escalating threat of deepfake audio by adapting self-supervised speech models into a Mixture-of-Experts architecture to enhance generalization against unseen synthesis methods. The study demonstrates that this architectural shift improves robustness across 14 datasets, significantly lowering error rates while preserving representations learned during pretraining. For AI security practitioners, this work highlights the viability of MoE structures in solving specific adversarial challenges within speech recognition systems without relying on quantum technologies or general consumer electronics trends.
From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing
arXiv:2606.14639v1 Announce Type: cross Abstract: Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging. A key limitation of current anti-spoofing systems is their limited robustness to unseen synthesis methods. In this work, we transform a self-supervised speech representation model into a Mixture-of-Experts (MoE) architecture to improve generalization. Feed-forward blocks in selected encoder layers are replaced by multiple expert networks controlled by a layer-wise gating mechanism, allowing experts to capture complementary acoustic patterns while preserving the representations learned during self-supervised pretraining. We further analyze the architectural choices affecting the performance of this MoE conversion and investigate the activation behavior of the experts. The proposed approach is evaluated on 14 spoofing datasets and reduces the macro EER from 5.46% to 4.81%, corresponding to 11.9% relative improvement over the baseline.
Abstract:Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging. A key limitation of current anti-spoofing systems is their limited robustness to unseen synthesis methods. In this work, we transform a self-supervised speech representation model into a Mixture-of-Experts (MoE) architecture to improve generalization. Feed-forward blocks in selected encoder layers are replaced by multiple expert networks controlled by a layer-wise gating mechanism, allowing experts to capture complementary acoustic patterns while preserving the representations learned during self-supervised pretraining. We further analyze the architectural choices affecting the performance of this MoE conversion and investigate the activation behavior of the experts. The proposed approach is evaluated on 14 spoofing datasets and reduces the macro EER from 5.46% to 4.81%, corresponding to 11.9% relative improvement over the baseline.
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2606.14639 [cs.SD]
(or arXiv:2606.14639v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2606.14639 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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