arXiv (All)
open access
Goodbye Equal Error Rate, Hello Local Information Disclosure: Evaluating Voice Anonymisation against 1-to-N Linkage Threats
Voice anonymisation aims to protect speaker identity. Currently, its empirical privacy evaluation heavily relies on the Equal Error Rate (EER). Originally designed for biometric verification, EER aggregates scores globally, implicitly assuming an attacker is only trying to verify if two specific voi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification#1012845
- Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification#965464
- Beyond fingerprint and voice print: cough sequence sound for identity verification#642425
- NullFace: Training-Free Localized Face Anonymization#153035
- Collapse, Not Invariance: Diagnosing Auxiliary Objectives in Speech Anti-Spoofing#1155474
Record · ID 984188
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive