F0 Modification via PV-TSM Algorithm for Speaker Anonymization Across Gender

Speaker anonymization has been developed to protect personally identifiable information w hile r etaining other encapsulated information in speech. Datasets, metrics, and protocols for evaluating speaker anonymization have been defined in the Voice Privacy Challenge (VPC). However, existing privacy metrics focus on evaluating general speaker individuality anonymization, which is represented by an x-vector. This study aims to investigate the effect of anonymization on the perception of gender. Understanding how anonymization caused gender transformation is essential for various applications of speaker anonymization. We proposed speaker anonymization methods across genders based on phase-vocoder time-scale modification (PV-TSM). Subsequently, in addition to the VPC evaluation, we developed a gender classifier toe valuate a speaker's gender anonymization. The objective evaluation results showed that our proposed method can successfully anonymize gender. In addition, our proposed methods outperformed the signal processing-based baseline methods in anonymizing speaker individuality represented by the x-vector in ASVeval while maintaining speech intelligibility.