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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.14012 |
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| _version_ | 1866918127314403328 |
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| author | Seo, Seungmin Aulov, Oleg Godil, Afzal Mangold, Kevin |
| author_facet | Seo, Seungmin Aulov, Oleg Godil, Afzal Mangold, Kevin |
| contents | 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 leakage with three complementary error rates: equal error rate, cumulative match characteristic hit rate, and embedding-space similarity measured via canonical correlation analysis and Procrustes analysis. Evaluation results reveal that all state-of-the-art speaker de-identification systems leak identity information. The highest performing system in our evaluation performs only slightly better than random guessing, while the lowest performing system 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Identity Leakage in Speaker De-Identification Systems Seo, Seungmin Aulov, Oleg Godil, Afzal Mangold, Kevin Sound Artificial Intelligence 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 leakage with three complementary error rates: equal error rate, cumulative match characteristic hit rate, and embedding-space similarity measured via canonical correlation analysis and Procrustes analysis. Evaluation results reveal that all state-of-the-art speaker de-identification systems leak identity information. The highest performing system in our evaluation performs only slightly better than random guessing, while the lowest performing system 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. |
| title | Evaluating Identity Leakage in Speaker De-Identification Systems |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2508.14012 |