'Voice Conversion' paper candidate 2409.09272

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• title: SafeEar: Content Privacy-Preserving Audio Deepfake Detection

• summary: Text-to-Speech (TTS) and Voice Conversion (VC) models have exhibited
  remarkable performance in generating realistic and natural audio. However,
  their dark side, audio deepfake poses a significant threat to both society and
  individuals. Existing countermeasures largely focus on determining the
  genuineness of speech based on complete original audio recordings, which
  however often contain private content. This oversight may refrain deepfake
  detection from many applications, particularly in scenarios involving sensitive
  information like business secrets. In this paper, we propose SafeEar, a novel
  framework that aims to detect deepfake audios without relying on accessing the
  speech content within. Our key idea is to devise a neural audio codec into a
  novel decoupling model that well separates the semantic and acoustic
  information from audio samples, and only use the acoustic information (e.g.,
  prosody and timbre) for deepfake detection. In this way, no semantic content
  will be exposed to the detector. To overcome the challenge of identifying
  diverse deepfake audio without semantic clues, we enhance our deepfake detector
  with real-world codec augmentation. Extensive experiments conducted on four
  benchmark datasets demonstrate SafeEar's effectiveness in detecting various
  deepfake techniques with an equal error rate (EER) down to 2.02%.
  Simultaneously, it shields five-language speech content from being deciphered
  by both machine and human auditory analysis, demonstrated by word error rates
  (WERs) all above 93.93% and our user study. Furthermore, our benchmark
  constructed for anti-deepfake and anti-content recovery evaluation helps
  provide a basis for future research in the realms of audio privacy preservation
  and deepfake detection.

• id: http://arxiv.org/abs/2409.09272v1

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