  In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker
text-to-speech model that improves similarity for speakers unseen during
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 converges using only 11
speakers, reaching state-of-the-art results for similarity with new speakers,
as well as high speech quality.
