  We propose a Text-to-Speech method to create an unseen expressive style using
one utterance of expressive speech of around one second. Specifically, we
enhance the disentanglement capabilities of a state-of-the-art
sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a
Householder Flow. The proposed system provides a 22% KL-divergence reduction
while jointly improving perceptual metrics over state-of-the-art. At synthesis
time we use one example of expressive style as a reference input to the encoder
for generating any text in the desired style. Perceptual MUSHRA evaluations
show that we can create a voice with a 9% relative naturalness improvement over
standard Neural Text-to-Speech, while also improving the perceived emotional
intensity (59 compared to the 55 of neutral speech).
