# Content Leakage in LibriSpeech and Its Impact on the Privacy Evaluation of Speaker Anonymization

arXiv: https://arxiv.org/abs/2601.13107 · HTML: https://mlsanity.com/paper/2601.13107/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 19 Jan 2026.

## Built on by (most cited first, 2 of 2)

- [Exploiting Acoustic and Content-Oriented Speaker Verification Attacks Against Multilingual Voice Anonymization](https://mlsanity.com/paper/2610.08107/) (2026)
- [VoxAnon: A balanced dataset for a fair evaluation of speech anonymization](https://mlsanity.com/paper/2610.13313/) (2026)

## Abstract (arXiv)

Speaker anonymization aims to conceal a speaker's identity, without considering the linguistic content. In this study, we reveal a weakness of Librispeech, the dataset that is commonly used to evaluate anonymizers: the books read by the Librispeech speakers are so distinct, that speakers can be identified by their vocabularies. Even perfect anonymizers cannot prevent this identity leakage. The EdAcc dataset is better in this regard: only a few speakers can be identified through their vocabularies, encouraging the attacker to look elsewhere for the identities of the anonymized speakers. EdAcc also comprises spontaneous speech and more diverse speakers, complementing Librispeech and giving more insights into how anonymizers work.