Adapting BERT as a text-only ASV attacker on VoicePrivacy datasets yields mean EER 35% (some speakers 2%), driven by semantic keyword overlaps from LibriSpeech curation, prompting calls to revise evaluation datasets and move beyond global EER.
The anonymization systems to be attacked are chosen such that their architectures and intermediate represen- tations are diverse
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You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks
Adapting BERT as a text-only ASV attacker on VoicePrivacy datasets yields mean EER 35% (some speakers 2%), driven by semantic keyword overlaps from LibriSpeech curation, prompting calls to revise evaluation datasets and move beyond global EER.