In this paper we propose Flowtron: an autoregressive flow-based generative
network for text-to-speech synthesis with control over speech variation and
style transfer. Flowtron borrows insights from IAF and revamps Tacotron in
order to provide high-quality and expressive mel-spectrogram synthesis.
Flowtron is optimized by maximizing the likelihood of the training data, which
makes training simple and stable. Flowtron learns an invertible mapping of data
to a latent space that can be manipulated to control many aspects of speech
synthesis (pitch, tone, speech rate, cadence, accent). Our mean opinion scores
(MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech
quality. In addition, we provide results on control of speech variation,
interpolation between samples and style transfer between speakers seen and
unseen during training. Code and pre-trained models will be made publicly
available at this https URL

本文提出了一种自回归基于流的生成网络 Flowtron，用于对语音的合成，并提供了控制语音变化和风格转移的功能。Flowtron 通过最大化训练数据的可能性进行优化，学习将数据映射到一个潜在空间，可以操纵语音合成的许多方面。与现有模型进行比较得出，Flowtron 在语音质量上与最先进的 TTS 模型相匹配。