Non-Autoregressive Neural Text-to-Speech

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao


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 its autoregressive counterpart 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 by applying various parallel neural vocoders, which can synthesize speech from text through a single feed-forward pass. We also explore a novel approach to train the IAF-based vocoder from scratch, which avoids the need for distillation from a separately trained WaveNet.