From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing

Voice synthesis technology has advanced to the point where synthetic speech is nearly indistinguishable from genuine recordings — a serious problem for voice authentication, call centers, and media verification. This paper transforms a self-supervised speech model into a Mixture-of-Experts architecture, where different specialist networks learn complementary acoustic cues for detecting spoofing. Evaluated across 14 spoofing datasets, it achieves an 11.9% relative improvement in error rate. Applications include fraud prevention in banking voice authentication, deepfake audio detection for journalism and legal evidence, broadcast media verification, and securing voice-controlled systems against adversarial impersonation attacks that grow more convincing as generative audio technology improves. Authors: Hugo Daumain, Driss Matrouf, Khaled Khelif, Mickael Rouvier Paper: https://arxiv.org/abs/2606.14639v1