标题： 无参考对抗性性别混淆语音 显示英文标题

标题： Reference-free Adversarial Sex Obfuscation in Speech

摘要： 语音中的性别转换涉及数据收集带来的隐私风险，并且即使在没有目标说话人参考的情况下，输出中通常仍会留下残留的性别特定线索。我们引入了RASO，即无参考对抗性性别混淆方法。创新之处包括一个性别条件对抗学习框架，用于将语言内容与与性别相关的声学标记分离，以及显式正则化，以将基频分布和共振峰轨迹与从性别平衡训练数据中学到的性别中性特征对齐。RASO保留语言内容，并且即使在半知情攻击模型下进行评估，它在性别混淆方面也显著优于一种竞争方法。 显示英文摘要

摘要： Sex conversion in speech involves privacy risks from data collection and often leaves residual sex-specific cues in outputs, even when target speaker references are unavailable. We introduce RASO for Reference-free Adversarial Sex Obfuscation. Innovations include a sex-conditional adversarial learning framework to disentangle linguistic content from sex-related acoustic markers and explicit regularisation to align fundamental frequency distributions and formant trajectories with sex-neutral characteristics learned from sex-balanced training data. RASO preserves linguistic content and, even when assessed under a semi-informed attack model, it significantly outperforms a competing approach to sex obfuscation.