  Although neural end-to-end text-to-speech models can synthesize highly
natural speech, there is still room for improvements to its efficiency and
naturalness. This paper proposes a non-autoregressive neural text-to-speech
model augmented with a variational autoencoder-based residual encoder. This
model, called \emph{Parallel Tacotron}, is highly parallelizable during both
training and inference, allowing efficient synthesis on modern parallel
hardware. The use of the variational autoencoder relaxes the one-to-many
mapping nature of the text-to-speech problem and improves naturalness. To
further improve the naturalness, we use lightweight convolutions, which can
efficiently capture local contexts, and introduce an iterative spectrogram loss
inspired by iterative refinement. Experimental results show that Parallel
Tacotron matches a strong autoregressive baseline in subjective evaluations
with significantly decreased inference time.
