Speech pseudonymisation assessment using voice similarity matrices

Noé, Paul-Gauthier; Bonastre, Jean-François; Matrouf, Driss; Tomashenko, Natalia; Nautsch, Andreas; Evans, Nicholas
INTERSPEECH 2020, 21st Annual Conference of the International Speech Communication Association, 25-29 October 2020, Shanghai, China (Virtual Conference)

The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications. These are essential since speech signals convey a wealth of rich, personal and potentially sensitive information. Anonymisation, the focus of the recent VoicePrivacy initiative, is one strategy to protect speaker identity information. Pseudonymisation solutions aim not only to mask the speaker identity and preserve the linguistic content, quality and naturalness, as is the goal of anonymisation, but also to preserve voice distinctiveness. Existing metrics for the assessment of anonymisation are ill-suited and those for the assessment of pseudonymisation are completely lacking. Based upon voice similarity matrices, this paper proposes the first intuitive visualisation of pseudonymisation performance for speech signals and two novel metrics for objective assessment. They reflect the two, key pseudonymisation requirements of de-identification and voice distinctiveness.
 
 

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Type:
Conference
City:
Shanghai
Date:
2020-10-24
Department:
Digital Security
Eurecom Ref:
6328
Copyright:
© ISCA. Personal use of this material is permitted. The definitive version of this paper was published in INTERSPEECH 2020, 21st Annual Conference of the International Speech Communication Association, 25-29 October 2020, Shanghai, China (Virtual Conference) and is available at :

PERMALINK : https://www.eurecom.fr/publication/6328