  Voice conversion is the task of converting a spoken utterance from a source
speaker so that it appears to be said by a different target speaker while
retaining the linguistic content of the utterance. Recent advances have led to
major improvements in the quality of voice conversion systems. However, to be
useful in a wider range of contexts, voice conversion systems would need to be
(i) trainable without access to parallel data, (ii) work in a zero-shot setting
where both the source and target speakers are unseen during training, and (iii)
run in real time or faster. Recent techniques fulfil one or two of these
requirements, but not all three. This paper extends recent voice conversion
models based on generative adversarial networks (GANs), to satisfy all three of
these conditions. We specifically extend the recent StarGAN-VC model by
conditioning it on a speaker embedding (from a potentially unseen speaker).
This allows the model to be used in a zero-shot setting, and we therefore call
it StarGAN-ZSVC. We compare StarGAN-ZSVC against other voice conversion
techniques in a low-resource setting using a small 9-minute training set.
Compared to AutoVC -- another recent neural zero-shot approach -- we observe
that StarGAN-ZSVC gives small improvements in the zero-shot setting, showing
that real-time zero-shot voice conversion is possible even for a model trained
on very little data. Further work is required to see whether scaling up
StarGAN-ZSVC will also improve zero-shot voice conversion quality in
high-resource contexts.
