results: 研究发现，随着攻击者技能的提高，声音匿名系统中的 subgroup 偏见变得更加严重，高亮了需要为各 subgroup 创建包容性的评测数据集和全面评估策略。Abstract
In an age of voice-enabled technology, voice anonymization offers a solution to protect people's privacy, provided these systems work equally well across subgroups. This study investigates bias in voice anonymization systems within the context of the Voice Privacy Challenge. We curate a novel benchmark dataset to assess performance disparities among speaker subgroups based on sex and dialect. We analyze the impact of three anonymization systems and attack models on speaker subgroup bias and reveal significant performance variations. Notably, subgroup bias intensifies with advanced attacker capabilities, emphasizing the challenge of achieving equal performance across all subgroups. Our study highlights the need for inclusive benchmark datasets and comprehensive evaluation strategies that address subgroup bias in voice anonymization.
摘要
在声音启用技术时代，声音匿名化提供了一种保护人们隐私的解决方案，只要这些系统在不同 subgroup 中工作 equally well 然后。这个研究在 Voice Privacy Challenge 的context中调查声音匿名化系统中 subgroup 偏见的问题。我们创建了一个新的 benchmark dataset 来评估 speaker subgroup 之间的性能差异。我们分析了三种匿名系统和三种攻击模型对 speaker subgroup 偏见的影响，发现了显著的性能差异。特别是在高级攻击者能力下， subgroup 偏见变得更加严重，这 подчерки着在所有 subgroup 中都实现等效性的挑战。我们的研究强调了 inclusive 的 benchmark dataset 和全面的评估策略，以解决声音匿名化中 subgroup 偏见的问题。