Speaker anonymisation using the McAdams coefficient

Patino, Jose; Tomashenko, Natalia; Todisco, Massimiliano; Nautsch, Andreas; Evans, Nicholas
INTERSPEECH 2021, Conference of the International Speech Communication Association, 30 August-3 September 2021, Brno, Czechia (Virtual Conference)

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.

 


DOI
HAL
Type:
Conférence
City:
Brno
Date:
2021-08-29
Department:
Sécurité numérique
Eurecom Ref:
6396
Copyright:
© ISCA. Personal use of this material is permitted. The definitive version of this paper was published in INTERSPEECH 2021, Conference of the International Speech Communication Association, 30 August-3 September 2021, Brno, Czechia (Virtual Conference) and is available at : http://dx.doi.org/10.21437/Interspeech.2021-1070

PERMALINK : https://www.eurecom.fr/publication/6396