  In this work, we propose ParaNet, a non-autoregressive seq2seq model that
converts text to spectrogram. It is fully convolutional and brings 46.7 times
speed-up over the lightweight Deep Voice 3 at synthesis, while obtaining
reasonably good speech quality. ParaNet also produces stable alignment between
text and speech on the challenging test sentences by iteratively improving the
attention in a layer-by-layer manner. Furthermore, we build the parallel
text-to-speech system and test various parallel neural vocoders, which can
synthesize speech from text through a single feed-forward pass. We also explore
a novel VAE-based approach to train the inverse autoregressive flow (IAF) based
parallel vocoder from scratch, which avoids the need for distillation from a
separately trained WaveNet as previous work.
