Content constraints improve reversible speaker anonymization against unseen verifiers
On 38 unseen speakers, attacker equal error rate rose from 2.8% to 26.3%, with 11.8% word error rate.
4 stories, newest first · one card per story
On 38 unseen speakers, attacker equal error rate rose from 2.8% to 26.3%, with 11.8% word error rate.
LLMs separate same-speaker pairs from text; 50-word probes weaken the strongest adversaries most.
Acoustic ASV attackers generally beat content-oriented ones against multilingual voice anonymization.
Voice-similarity alignment tracks embedding dimensionality, not verification EER; a bottleneck lifts it from 0.08 to 0.74.
Today 1.5K readers · 2.4K views · 757 reads · all time 189K views