Anonymisation has the goal of manipulating speech signals in order to degrade
the reliability of automatic approaches to speaker recognition, while
preserving other aspects of speech, such as those relating to intelligibility
and naturalness. This paper reports an approach to anonymisation that, unlike
other current approaches, requires no training data, is based upon well-known
signal processing techniques and is both efficient and effective. The proposed
solution uses the McAdams coefficient to transform the spectral envelope of
speech signals. Results derived using common VoicePrivacy 2020 databases and
protocols show that random, optimised transformations can outperform competing
solutions in terms of anonymisation while causing only modest, additional
degradations to intelligibility, even in the case of a semi-informed privacy
adversary.

本研究提出了一种无需训练数据的语音匿名化方法，利用 McAdams 系数转换语音信号的频谱包络，实现对说话人的匿名化保护，该方法不仅高效有效，而且在半知情的隐私对手下，成功降低了可靠的声纹识别，仅对可懂性产生少量的降级。