Listen to the Features: Voice Anonymization Driven by Content Embedding Matching over Signal Reconstruction
The paper presents a voice anonymization model focusing on preserving content rather than producing realistic speech. It relies on content embeddings extracted from a frozen pretrained wav2vec2 encoder. These embeddings are decoded into an anonymized signal using vector quantization and a HiFi-GAN vocoder, both trained on LibriTTS without any waveform reconstruction loss or speaker embedding mapping. The training objective enforces that embeddings of the anonymized signal match those of the original one. While training, an auxiliary speaker classification branch with a gradient reversal layer is used to discard speakerspecific information. Results show that this straightforward embedding-...
| Authors | Adrien Schneider, Kacper Zabkowski, Anderson Augusma et al. |
|---|---|
| Primary category | eess.SP |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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