  Artificial speech synthesis has made a great leap in terms of naturalness as
recent Text-to-Speech (TTS) systems are capable of producing speech with
similar quality to human recordings. However, not all speaking styles are easy
to model: highly expressive voices are still challenging even to recent TTS
architectures since there seems to be a trade-off between expressiveness in a
generated audio and its signal quality. In this paper, we present a set of
techniques that can be leveraged to enhance the signal quality of a
highly-expressive voice without the use of additional data. The proposed
techniques include: tuning the autoregressive loop's granularity during
training; using Generative Adversarial Networks in acoustic modelling; and the
use of Variational Auto-Encoders in both the acoustic model and the neural
vocoder. We show that, when combined, these techniques greatly closed the gap
in perceived naturalness between the baseline system and recordings by 39% in
terms of MUSHRA scores for an expressive celebrity voice.
