Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of information about a speaker's gender, a privacy-sensitive attribute. Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i.e., perturbations are generated and tested with a neural network. In this paper, we propose to go beyond this practice to strengthen the study of gender protection. First, we demonstrate the importance of testing gender inference attacks that are based on speech features historically developed by speech scientists, alongside the conventionally used neural classifiers. Next, we argue that researchers should use speech features to gain insight into how protective modifications change the speech signal. Finally, we point out that gender-protection algorithms should be compared with novel "vocal adversaries", human-executed voice adaptations, in order to improve interpretability and enable before-the-mic protection.

翻译：近期研究提出了修改语音以防御性别推断攻击的方法。这些保护算法的目标是控制说话人性别信息（一种隐私敏感属性）的可获取性。目前，开发和测试性别保护算法的常见做法是“神经对神经”，即通过神经网络生成扰动并验证其有效性。本文主张超越这一常规实践，以强化性别保护研究。首先，我们论证了在传统神经分类器基础上，引入语音科学家历史开发的语音特征进行性别推断攻击测试的重要性。其次，我们指出研究者应利用语音特征深入理解保护性修改如何改变语音信号。最后，我们强调性别保护算法应与新型“声音对抗”（即人类执行的语音适应性修改）进行比较，以提升可解释性并实现前置麦克风保护。