In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen in training. We propose a speaker-conditional architecture that explores a flow-based decoder that works in a zero-shot scenario. As text encoders, we explore a dilated residual convolutional-based encoder, gated convolutional-based encoder, and transformer-based encoder. Additionally, we have shown that adjusting a GAN-based vocoder for the spectrograms predicted by the TTS model on the training dataset can significantly improve the similarity and speech quality for new speakers. Our model is able to converge in training, using only 11 speakers, reaching state-of-the-art results for similarity with new speakers, as well as high speech quality.
翻译：在本文中,我们提出了SC-GlowTTS:一个高效的零点多发多发语音文本到语音模型,该模型可以改善培训中看不见的发言者的相似性。我们提出了一个语音条件结构,用于探索一个在零点情况下起作用的基于流动的解码器。作为文本编码器,我们探索了一种基于革命的剩余编码器、基于门的基于革命的编码器和基于变压器的编码器。此外,我们已经表明,根据TTS模型在培训数据集上预测的光谱调整一个基于GAN的vocoder可以大大改善新发言者的相似性和语言质量。我们的模型能够集中到培训中,仅使用11位发言者,为与新发言者的相似性以及高语言质量达到最新水平的结果。
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