ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Speaker Anonymisation Using the McAdams Coefficient

Jose Patino, Natalia Tomashenko, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans

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.

doi: 10.21437/Interspeech.2021-1070

Cite as: Patino, J., Tomashenko, N., Todisco, M., Nautsch, A., Evans, N. (2021) Speaker Anonymisation Using the McAdams Coefficient. Proc. Interspeech 2021, 1099-1103, doi: 10.21437/Interspeech.2021-1070

  author={Jose Patino and Natalia Tomashenko and Massimiliano Todisco and Andreas Nautsch and Nicholas Evans},
  title={{Speaker Anonymisation Using the McAdams Coefficient}},
  booktitle={Proc. Interspeech 2021},