Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of
  Synthesized Speech Under Distribution Shifts
We address the challenge of detecting synthesized speech under distribution
shifts -- arising from unseen synthesis methods, speakers, languages, or audio
conditions -- relative to the training data. Few-shot learning methods are a
promising way to tackle distribution shifts by rapidly adapting on the basis of
a few in-distribution samples. We propose a self-attentive prototypical network
to enable more robust few-shot adaptation. To evaluate our approach, we
systematically compare the performance of traditional zero-shot detectors and
the proposed few-shot detectors, carefully controlling training conditions to
introduce distribution shifts at evaluation time. In conditions where
distribution shifts hamper the zero-shot performance, our proposed few-shot
adaptation technique can quickly adapt using as few as 10 in-distribution
samples -- achieving upto 32% relative EER reduction on deepfakes in Japanese
language and 20% relative reduction on ASVspoof 2021 Deepfake dataset.
68a5dbdf9e4b49496aac684f
Ashi Garg
claimed_verified
2025-08-20T19:11:40.862Z
6446ab9815a27291ef8b7313
https://cdn-avatars.huggingface.co/v1/production/uploads/6446ab9815a27291ef8b7313/rw5xK-BV2jyct0G3ft13a.png
Ashi Garg
user
ash56
68a5dbdf9e4b49496aac6850
Zexin Cai
68a5dbdf9e4b49496aac6851
Henry Li Xinyuan
68a5dbdf9e4b49496aac6852
Leibny Paola García-Perera
68a5dbdf9e4b49496aac6853
Kevin Duh
68a5dbdf9e4b49496aac6854
Sanjeev Khudanpur
68a5dbdf9e4b49496aac6855
Matthew Wiesner
68a5dbdf9e4b49496aac6856
Nicholas Andrews
Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of
  Synthesized Speech Under Distribution Shifts
We address the challenge of detecting synthesized speech under distribution
shifts -- arising from unseen synthesis methods, speakers, languages, or audio
conditions -- relative to the training data. Few-shot learning methods are a
promising way to tackle distribution shifts by rapidly adapting on the basis of
a few in-distribution samples. We propose a self-attentive prototypical network
to enable more robust few-shot adaptation. To evaluate our approach, we
systematically compare the performance of traditional zero-shot detectors and
the proposed few-shot detectors, carefully controlling training conditions to
introduce distribution shifts at evaluation time. In conditions where
distribution shifts hamper the zero-shot performance, our proposed few-shot
adaptation technique can quickly adapt using as few as 10 in-distribution
samples -- achieving upto 32% relative EER reduction on deepfakes in Japanese
language and 20% relative reduction on ASVspoof 2021 Deepfake dataset.
A self-attentive prototypical network improves few-shot adaptation for detecting synthesized speech under distribution shifts, achieving significant performance gains over zero-shot detectors.
self-attentive prototypical network few-shot learning distribution shifts zero-shot detectors deepfakes ASVspoof 2021 EER reduction