用不同假身份可更好隐藏说话人，提升隐私保护效果。
Pinhole Effect on Linkability and Dispersion in Speaker Anonymization
- 为每段语音分配不同假身份，而非统一假身份。
- 相比统一假身份，分散度更高且更难关联原身份。
- 提出‘针孔效应’解释策略差异，适合隐私安全研究者。
说话人匿名化旨在隐藏语音中的个体特征，使匿名语音无法回溯到原始说话人。近期方法通过将语音解耦为内容与说话人成分，用伪说话人替换真实说话人。匿名语音可映射至跨语句共享的通用伪说话人，或每个语句独有的独立伪说话人。本文研究两种映射策略对三个关键维度的影响：说话人可关联性、匿名说话人空间的分散度、以及对原始身份的去标识程度。结果表明，使用独立伪说话人相比通用伪说话人，能提高说话人分散度并降低可关联性，从而增强隐私保护。这一现象通过提出的‘针孔效应’概念框架进行解释，该框架阐明了映射策略与匿名化性能之间的关系，并经实证评估验证。
原文摘要 · Abstract (English)
Speaker anonymization aims to conceal speaker-specific attributes in speech signals, making the anonymized speech unlinkable to the original speaker identity. Recent approaches achieve this by disentangling speech into content and speaker components, replacing the latter with pseudo speakers. The anonymized speech can be mapped either to a common pseudo speaker shared across utterances or to distinct pseudo speakers unique to each utterance. This paper investigates the impact of these mapping strategies on three key dimensions: speaker linkability, dispersion in the anonymized speaker space, and de-identification from the original identity. Our findings show that using distinct pseudo speakers increases speaker dispersion and reduces linkability compared to common pseudo-speaker mapping, thereby enhancing privacy preservation. These observations are interpreted through the proposed pinhole effect, a conceptual framework introduced to explain the relationship between mapping strategies and anonymization performance. The hypothesis is validated through empirical evaluation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。