  We present a Cycle-GAN based many-to-many voice conversion method that can
convert between speakers that are not in the training set. This property is
enabled through speaker embeddings generated by a neural network that is
jointly trained with the Cycle-GAN. In contrast to prior work in this domain,
our method enables conversion between an out-of-dataset speaker and a target
speaker in either direction and does not require re-training. Out-of-dataset
speaker conversion quality is evaluated using an independently trained speaker
identification model, and shows good style conversion characteristics for
previously unheard speakers. Subjective tests on human listeners show style
conversion quality for in-dataset speakers is comparable to the
state-of-the-art baseline model.
