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.

本文提出了一种用于神经端到端文本到语音的非自回归神经网络模型 Parallel Tacotron，该模型采用变分自编码器来提高自然性，轻量级卷积来捕获局部上下文，并引入迭代光谱损失以进一步提高自然性，实验结果表明，Parallel Tacotron 与强的自回归基线模型相匹配，并在推理时间上显著提高了效率。