  Speaker anonymization aims to protect the privacy of speakers while
preserving spoken linguistic information from speech. Current mainstream neural
network speaker anonymization systems are complicated, containing an F0
extractor, speaker encoder, automatic speech recognition acoustic model (ASR
AM), speech synthesis acoustic model and speech waveform generation model.
Moreover, as an ASR AM is language-dependent, trained on English data, it is
hard to adapt it into another language. In this paper, we propose a simpler
self-supervised learning (SSL)-based method for language-independent speaker
anonymization without any explicit language-dependent model, which can be
easily used for other languages. Extensive experiments were conducted on the
VoicePrivacy Challenge 2020 datasets in English and AISHELL-3 datasets in
Mandarin to demonstrate the effectiveness of our proposed SSL-based
language-independent speaker anonymization method.
