  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.
