  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.
