  In speaker anonymization, speech recordings are modified in a way that the
identity of the speaker remains hidden. While this technology could help to
protect the privacy of individuals around the globe, current research restricts
this by focusing almost exclusively on English data. In this study, we extend a
state-of-the-art anonymization system to nine languages by transforming
language-dependent components to their multilingual counterparts. Experiments
testing the robustness of the anonymized speech against privacy attacks and
speech deterioration show an overall success of this system for all languages.
The results suggest that speaker embeddings trained on English data can be
applied across languages, and that the anonymization performance for a language
is mainly affected by the quality of the speech synthesis component used for
it.
