  Some recent studies have demonstrated the feasibility of single-stage neural
text-to-speech, which does not need to generate mel-spectrograms but generates
the raw waveforms directly from the text. Single-stage text-to-speech often
faces two problems: a) the one-to-many mapping problem due to multiple speech
variations and b) insufficiency of high frequency reconstruction due to the
lack of supervision of ground-truth acoustic features during training. To solve
the a) problem and generate more expressive speech, we propose a novel
phoneme-level prosody modeling method based on a variational autoencoder with
normalizing flows to model underlying prosodic information in speech. We also
use the prosody predictor to support end-to-end expressive speech synthesis.
Furthermore, we propose the dual parallel autoencoder to introduce supervision
of the ground-truth acoustic features during training to solve the b) problem
enabling our model to generate high-quality speech. We compare the synthesis
quality with state-of-the-art text-to-speech systems on an internal expressive
English dataset. Both qualitative and quantitative evaluations demonstrate the
superiority and robustness of our method for lossless speech generation while
also showing a strong capability in prosody modeling.

Sound;Audio and Speech Processing