标题： 通过对抗学习提高说话人匿名化评估对目标说话人的鲁棒性 显示英文标题

标题： Improving the Speaker Anonymization Evaluation's Robustness to Target Speakers with Adversarial Learning

摘要： 当前对说话人匿名化的隐私评估在使用同性别目标选择算法（TSA）时往往高估了隐私性，尽管这种TSA泄露了说话人的性别，因此应该更加脆弱。 我们假设这是因为在评估中没有考虑到匿名语音同时包含源说话人和目标说话人的信息。 为了解决这个问题，我们提出添加一个目标分类器，用于衡量目标说话人信息在评估中的影响，该分类器也可以通过对抗学习被移除。 实验表明，这种方法对多个匿名化器有效，尤其是在使用同性别TSA时，从而实现了更可靠的评估。 显示英文摘要

摘要： The current privacy evaluation for speaker anonymization often overestimates privacy when a same-gender target selection algorithm (TSA) is used, although this TSA leaks the speaker's gender and should hence be more vulnerable. We hypothesize that this occurs because the evaluation does not account for the fact that anonymized speech contains information from both the source and target speakers. To address this, we propose to add a target classifier that measures the influence of target speaker information in the evaluation, which can also be removed with adversarial learning. Experiments demonstrate that this approach is effective for multiple anonymizers, particularly when using a same-gender TSA, leading to a more reliable assessment.