Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of
  Synthesized Speech Under Distribution Shifts
Authors: name: Ashi Garg, name: Zexin Cai, name: Henry Li Xinyuan, name: Leibny Paola García-Perera, name: Kevin Duh, name: Sanjeev Khudanpur, name: Matthew Wiesner, name: Nicholas Andrews

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.