  We introduce a novel method to improve the performance of the VoicePrivacy
Challenge 2022 baseline B1 variants. Among the known deficiencies of
x-vector-based anonymization systems is the insufficient disentangling of the
input features. In particular, the fundamental frequency (F0) trajectories,
which are used for voice synthesis without any modifications. Especially in
cross-gender conversion, this situation causes unnatural sounding voices,
increases word error rates (WERs), and personal information leakage. Our
submission overcomes this problem by synthesizing an F0 trajectory, which
better harmonizes with the anonymized x-vector. We utilized a low-complexity
deep neural network to estimate an appropriate F0 value per frame, using the
linguistic content from the bottleneck features (BN) and the anonymized
x-vector. Our approach results in a significantly improved anonymization system
and increased naturalness of the synthesized voice. Consequently, our results
suggest that F0 extraction is not required for voice anonymization.
