  Speaker anonymization aims to conceal a speaker's identity without degrading
speech quality and intelligibility. Most speaker anonymization systems
disentangle the speaker representation from the original speech and achieve
anonymization by averaging or modifying the speaker representation. However,
the anonymized speech is subject to reduction in pseudo speaker
distinctiveness, speech quality and intelligibility for out-of-distribution
speaker. To solve this issue, we propose SALT, a Speaker Anonymization system
based on Latent space Transformation. Specifically, we extract latent features
by a self-supervised feature extractor and randomly sample multiple speakers
and their weights, and then interpolate the latent vectors to achieve speaker
anonymization. Meanwhile, we explore the extrapolation method to further extend
the diversity of pseudo speakers. Experiments on Voice Privacy Challenge
dataset show our system achieves a state-of-the-art distinctiveness metric
while preserving speech quality and intelligibility. Our code and demo is
availible at https://github.com/BakerBunker/SALT .
