  In order to protect the privacy of speech data, speaker anonymization aims
for hiding the identity of a speaker by changing the voice in speech
recordings. This typically comes with a privacy-utility trade-off between
protection of individuals and usability of the data for downstream
applications. One of the challenges in this context is to create non-existent
voices that sound as natural as possible.
  In this work, we propose to tackle this issue by generating speaker
embeddings using a generative adversarial network with Wasserstein distance as
cost function. By incorporating these artificial embeddings into a
speech-to-text-to-speech pipeline, we outperform previous approaches in terms
of privacy and utility. According to standard objective metrics and human
evaluation, our approach generates intelligible and content-preserving yet
privacy-protecting versions of the original recordings.
