  Speaker anonymization is an effective privacy protection solution designed to
conceal the speaker's identity while preserving the linguistic content and
para-linguistic information of the original speech. While most prior studies
focus solely on a single language, an ideal speaker anonymization system should
be capable of handling multiple languages. This paper proposes MUSA, a
Multi-lingual Speaker Anonymization approach that employs a serial
disentanglement strategy to perform a step-by-step disentanglement from a
global time-invariant representation to a temporal time-variant representation.
By utilizing semantic distillation and self-supervised speaker distillation,
the serial disentanglement strategy can avoid strong inductive biases and
exhibit superior generalization performance across different languages.
Meanwhile, we propose a straightforward anonymization strategy that employs
empty embedding with zero values to simulate the speaker identity concealment
process, eliminating the need for conversion to a pseudo-speaker identity and
thereby reducing the complexity of speaker anonymization process. Experimental
results on VoicePrivacy official datasets and multi-lingual datasets
demonstrate that MUSA can effectively protect speaker privacy while preserving
linguistic content and para-linguistic information.
