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The proliferation of speech technologies and rising privacy legislation callsfor the development of privacy preservation solutions for speech applications.These are essential since speech signals convey a wealth of rich, personal andpotentially sensitive information. Anonymisation, the focus of the recentVoicePrivacy initiative, is one strategy to protect speaker identityinformation. Pseudonymisation solutions aim not only to mask the speakeridentity and preserve the linguistic content, quality and naturalness, as isthe goal of anonymisation, but also to preserve voice distinctiveness. Existingmetrics for the assessment of anonymisation are ill-suited and those for theassessment of pseudonymisation are completely lacking. Based upon voicesimilarity matrices, this paper proposes the first intuitive visualisation ofpseudonymisation performance for speech signals and two novel metrics forobjective assessment. They reflect the two, key pseudonymisation requirementsof de-identification and voice distinctiveness.

Speech Pseudonymisation Assessment Using Voice Similarity Matrices