arXiv:2606.16837v1 Announce Type: cross Abstract: Spoofed speech detection is increasingly challenged by realistic synthesis, voice conversion, and replay attacks, with cross-dataset generalization remaining a major limitation. This work we propose a Temporal Pyramid Adapter that utilize parallel temporal convolutions with varying receptive fields to capture multi-scale spoofing cues, ranging from local artifacts to global prosodic irregularities. We also integrated self-supervised XLS-R representations combined with front-end adapters, including Mel, Sinc, and a Temporal Pyramid design for mu
The proliferation of realistic AI-generated and manipulated audio necessitates advanced detection methods to maintain trust and security in digital communications.
This research contributes to the ongoing arms race against sophisticated spoofing attacks, critical for industries reliant on voice authentication and for combating misinformation.
The development of more robust spoofed speech detection systems makes it harder for adversarial AI to successfully impersonate individuals or spread disinformation via audio.
- · Cybersecurity industry
- · Financial services (voice authentication)
- · Governments (election integrity)
- · Law enforcement
- · Malicious actors
- · Creators of voice synthesis/conversion for illicit purposes
Improved detection capabilities will help mitigate the immediate threat of audio-based spoofing attacks.
This could lead to increased public confidence in voice-based authentication systems and digital audio content veracity.
It might also spur further investment in multi-modal identity verification beyond just audio to create more resilient security protocols.
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Read at arXiv cs.AI