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PUBLICATIONS

EVALUATING IDENTITY LEAKAGE IN SPEAKER DE-IDENTIFICATION SYSTEMS

Published

Author(s)

Seungmin Seo, Oleg Aulov, Afzal Godil, Kevin Mangold

Abstract

Speaker de-identification aims to conceal a speaker's identity while preserving intelligibility of the underlying speech. We introduce a benchmark that quantifies residual identity leak- age with three complementary error rates: equal error rate (EER), cumulative match characteristic (CMC) hit rate, and embedding-space similarity measured via canonical correla- tion analysis (CCA) and Procrustes analysis. Evaluation re- sults reveal that all state-of-the-art speaker de-identification systems leak identity information. The strongest system per- forms only slightly better than random guessing, while the weakest achieves a 45% hit rate within the top 50 candidates based on CMC. These findings highlight persistent privacy risks in current speaker de-identification technologies.
Proceedings Title
ICASSP 2026 - IEEE International Conference on Acoustics, Speech, and Signal Processing
Conference Dates
May 4-8, 2026
Conference Location
Barcelona, ES
Pub Type
Conferences

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Local Download
Cybersecurity and privacy, Information technology, Biometrics and Artificial intelligence

Citation

Seo, S. , Aulov, O. , Godil, A. and Mangold, K. (2025), EVALUATING IDENTITY LEAKAGE IN SPEAKER DE-IDENTIFICATION SYSTEMS, ICASSP 2026 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, ES, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960507 (Accessed October 16, 2025)

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Created August 21, 2025, Updated August 29, 2025
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