SOURCE-LINKED INTELLIGENCE

Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Speaker de-identification (SDID) aims to preserve privacy by concealing speaker identity while maintaining speech utility. However, current evaluations often reduce privacy to a single dimension - biometric verification performance - typically measured by Equal Error Rate (EER). This narrow focus ignores critical leakage channels, such as soft biometric inference, embedding-level re-identification, and structural template similarity, which threaten the unlinkability and irreversibility of biometric references. We propose a holistic evaluation framework across five complementary metrics: (i) EE

Read original source ↗ Open in workspace

recordType

paper

region

Global