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This study investigates the impact of gender information on utility, privacy,and fairness in voice biometric systems, guided by the General Data ProtectionRegulation (GDPR) mandates, which underscore the need for minimizing theprocessing and storage of private and sensitive data, and ensuring fairness inautomated decision-making systems. We adopt an approach that involves thefine-tuning of the wav2vec 2.0 model for speaker verification tasks, evaluatingpotential gender-related privacy vulnerabilities in the process. Genderinfluences during the fine-tuning process were employed to enhance fairness andprivacy in order to emphasise or obscure gender information within thespeakers' embeddings. Results from VoxCeleb datasets indicate our adversarialmodel increases privacy against uninformed attacks, yet slightly diminishesspeaker verification performance compared to the non-adversarial model.However, the model's efficacy reduces against informed attacks. Analysis ofsystem performance was conducted to identify potential gender biases, thushighlighting the need for further research to understand and improve thedelicate interplay between utility, privacy, and equity in voice biometricsystems.

Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0