  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.
