  Neural text-to-speech synthesis (NTTS) models have shown significant progress
in generating high-quality speech, however they require a large quantity of
training data. This makes creating models for multiple styles expensive and
time-consuming. In this paper different styles of speech are analysed based on
prosodic variations, from this a model is proposed to synthesise speech in the
style of a newscaster, with just a few hours of supplementary data. We pose the
problem of synthesising in a target style using limited data as that of
creating a bi-style model that can synthesise both neutral-style and
newscaster-style speech via a one-hot vector which factorises the two styles.
We also propose conditioning the model on contextual word embeddings, and
extensively evaluate it against neutral NTTS, and neutral concatenative-based
synthesis. This model closes the gap in perceived style-appropriateness between
natural recordings for newscaster-style of speech, and neutral speech synthesis
by approximately two-thirds.
