  Voice conversion is a challenging task which transforms the voice
characteristics of a source speaker to a target speaker without changing
linguistic content. Recently, there have been many works on many-to-many Voice
Conversion (VC) based on Variational Autoencoder (VAEs) achieving good results,
however, these methods lack the ability to disentangle speaker identity and
linguistic content to achieve good performance on unseen speaker scenarios. In
this paper, we propose a new method based on feature disentanglement to tackle
many to many voice conversion. The method has the capability to disentangle
speaker identity and linguistic content from utterances, it can convert from
many source speakers to many target speakers with a single autoencoder network.
Moreover, it naturally deals with the unseen target speaker scenarios. We
perform both objective and subjective evaluations to show the competitive
performance of our proposed method compared with other state-of-the-art models
in terms of naturalness and target speaker similarity.
