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 ex
The increased sophistication of synthetic speech necessitates more advanced anti-spoofing mechanisms, making the robustness of these systems a critical and timely concern.
Improving the robustness of anti-spoofing systems is crucial for maintaining trust in digital voice interactions, preventing fraud, and securing critical applications against highly realistic AI-generated spoofs.
This research introduces a novel architectural approach, converting self-supervised speech models into Mixture-of-Experts, offering a scalable pathway to more resilient anti-spoofing against unknown synthesis methods.
- · Cybersecurity sector
- · Financial institutions
- · Voice authentication providers
- · Speech technology developers
- · Malicious actors using synthetic speech
- · Outdated anti-spoofing solutions
Increased difficulty for attackers to bypass voice-based security systems using sophisticated synthetic speech.
Greater public and institutional confidence in biometric voice authentication and remote interactions.
Accelerated development of even more advanced AI-driven defenses and corresponding offensive techniques, creating an arms race in digital voice security.
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Read at arXiv cs.AI