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 wellknown
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.
