  Speech pseudonymization aims at altering a speech signal to map the
identifiable personal characteristics of a given speaker to another identity.
In other words, it aims to hide the source speaker identity while preserving
the intelligibility of the spoken content. This study takes place in the
VoicePrivacy 2020 challenge framework, where the baseline system performs
pseudonymization by modifying x-vector information to match a target speaker
while keeping the fundamental frequency (F0) unchanged. We propose to alter
other paralin-guistic features, here F0, and analyze the impact of this
modification across gender. We found that the proposed F0 modification always
improves pseudonymization We observed that both source and target speaker
genders affect the performance gain when modifying the F0.
