Speech Pseudonymisation Assessment Using Voice Similarity Matrices

by   Paul-Gauthier Noé, et al.

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.


SVSNet: An End-to-end Speaker Voice Similarity Assessment Model

Neural evaluation metrics derived for numerous speech generation tasks h...

Towards the Objective Speech Assessment of Smoking Status based on Voice Features: A Review of the Literature

In smoking cessation clinical research and practice, objective validatio...

The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment

Mounting privacy legislation calls for the preservation of privacy in sp...

Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers

In this paper, we build a speech privacy attack that exploits speech rev...

The Road Not Taken: Re-thinking the Feasibility of Voice Calling Over Tor

Anonymous VoIP calls over the Internet holds great significance for priv...

Benchmarking and challenges in security and privacy for voice biometrics

For many decades, research in speech technologies has focused upon impro...

Speaker De-identification System using Autoencodersand Adversarial Training

The fast increase of web services and mobile apps, which collect persona...