  Whilst recent neural text-to-speech (TTS) approaches produce high-quality
speech, they typically require a large amount of recordings from the target
speaker. In previous work, a 3-step method was proposed to generate
high-quality TTS while greatly reducing the amount of data required for
training. However, we have observed a ceiling effect in the level of
naturalness achievable for highly expressive voices when using this approach.
In this paper, we present a method for building highly expressive TTS voices
with as little as 15 minutes of speech data from the target speaker. Compared
to the current state-of-the-art approach, our proposed improvements close the
gap to recordings by 23.3% for naturalness of speech and by 16.3% for speaker
similarity. Further, we match the naturalness and speaker similarity of a
Tacotron2-based full-data (~10 hours) model using only 15 minutes of target
speaker data, whereas with 30 minutes or more, we significantly outperform it.
The following improvements are proposed: 1) changing from an autoregressive,
attention-based TTS model to a non-autoregressive model replacing attention
with an external duration model and 2) an additional Conditional Generative
Adversarial Network (cGAN) based fine-tuning step.
