In speaker anonymization, speech recordings are modified in a way that the identity of the speaker remains hidden. While this technology could help to protect the privacy of individuals around the globe, current research restricts this by focusing almost exclusively on English data. In this study, we extend a state-of-the-art anonymization system to nine languages by transforming language-dependent components to their multilingual counterparts. Experiments testing the robustness of the anonymized speech against privacy attacks and speech deterioration show an overall success of this system for all languages. The results suggest that speaker embeddings trained on English data can be applied across languages, and that the anonymization performance for a language is mainly affected by the quality of the speech synthesis component used for it.

翻译：在说话人匿名化技术中，语音记录经过修改以隐藏说话者的身份。尽管这项技术有助于保护全球个体的隐私，但当前研究几乎完全集中于英语数据，限制了其应用范围。本研究通过将语言相关组件转换为多语言对应模块，将一种先进的匿名化系统扩展至九种语言。通过测试匿名化语音在隐私攻击和语音质量退化方面的鲁棒性，实验表明该系统在所有语言中均取得总体成功。结果表明，基于英语数据训练的说话人嵌入可跨语言应用，且特定语言的匿名化性能主要受其语音合成组件质量的影响。