  While recent neural text-to-speech (TTS) systems perform remarkably well,
they typically require a substantial amount of recordings from the target
speaker reading in the desired speaking style. In this work, we present a novel
3-step methodology to circumvent the costly operation of recording large
amounts of target data in order to build expressive style voices with as little
as 15 minutes of such recordings. First, we augment data via voice conversion
by leveraging recordings in the desired speaking style from other speakers.
Next, we use that synthetic data on top of the available recordings to train a
TTS model. Finally, we fine-tune that model to further increase quality. Our
evaluations show that the proposed changes bring significant improvements over
non-augmented models across many perceived aspects of synthesised speech. We
demonstrate the proposed approach on 2 styles (newscaster and conversational),
on various speakers, and on both single and multi-speaker models, illustrating
the robustness of our approach.
