  The availability of data in expressive styles across languages is limited,
and recording sessions are costly and time consuming. To overcome these issues,
we demonstrate how to build low-resource, neural text-to-speech (TTS) voices
with only 1 hour of conversational speech, when no other conversational data
are available in the same language. Assuming the availability of non-expressive
speech data in that language, we propose a 3-step technology: 1) we train an
F0-conditioned voice conversion (VC) model as data augmentation technique; 2)
we train an F0 predictor to control the conversational flavour of the
voice-converted synthetic data; 3) we train a TTS system that consumes the
augmented data. We prove that our technology enables F0 controllability, is
scalable across speakers and languages and is competitive in terms of
naturalness over a state-of-the-art baseline model, another augmented method
which does not make use of F0 information.
