Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck
arXiv:2606.08678v1 Announce Type: cross Abstract: Sophisticated generative speech technology can undermined the reliability of voice biometrics. While spoofing detection systems excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. In this paper, we show that such issues could be caused by speaker bias, where models learn individual voice traits rather than markers of manipulation or generation. We propose a teacher-student framework for speaker-invariant spoofing detection that disentangles identity without requiring speaker labels. We leverag
The rapid advancement of generative AI in speech synthesis necessitates improved spoofing detection methods to maintain the integrity of voice biometrics.
This research directly addresses a critical security vulnerability in voice-based authentication systems, which are increasingly adopted across various sectors.
The ability to develop more robust speaker-invariant spoofing detection could significantly enhance the reliability and trustworthiness of voice biometrics for security and access control.
- · Voice biometric industry
- · Cybersecurity sector
- · Financial services
- · Government agencies
- · Malicious actors
- · Generative AI misuse
Improved defense against sophisticated audio deepfakes and voice impersonation.
Increased consumer and institutional confidence in voice-activated security and authentication systems.
Accelerated adoption of voice biometrics in high-stakes applications, potentially reducing reliance on other authentication methods.
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