Speech Pseudonymisation Assessment Using Voice Similarity Matrices

TL;DR

This paper introduces a novel framework using voice similarity matrices to visually assess speech pseudonymisation, along with new metrics to evaluate de-identification and voice distinctiveness, addressing a gap in current evaluation methods.

Contribution

It presents the first visualisation method and objective metrics specifically designed for assessing speech pseudonymisation performance.

Findings

Introduces voice similarity matrices for pseudonymisation assessment

Develops two novel metrics for de-identification and voice distinctiveness

Provides a comprehensive evaluation framework for speech pseudonymisation

Abstract

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.