Modeling Pseudo-Speaker Uncertainty in Voice Anonymization

Voice anonymization refers to the goal of suppressing personally identifiable voice attributes in speech. State-of-the-art models based on the voice conversion framework accomplish this goal by replacing the voice attributes of the speaker with those of a pseudo-speaker. This paper proposes to exploit the uncertainty estimate of pseudo-speaker in voice anonymization. For each target speaker, a pseudo-speaker distribution, characterized by a point estimate and its uncertainty, is estimated from a selected set of cohort speakers. Based on this distribution, a pseudo-speaker vector is sampled and used to replace the voice attributes in an anonymized speech. The efficacy of the proposed method was validated in the framework as provided by VoicePrivacy Challenge 2022. Audio samples can be found in https://voiceprivacy.github.io/pseudo-speaker-vector/.