SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

Edresson Casanova; Christopher Shulby; Eren Gölge; Nicolas Michael Müller; Frederico Santos de Oliveira; Arnaldo Candido Junior; Anderson da Silva Soares; Sandra Maria Aluisio; Moacir Antonelli Ponti

arXiv · 2104.05557

SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

Edresson Casanova·Christopher Shulby·Eren Gölge·Nicolas Michael Müller·Frederico Santos de Oliveira·Arnaldo Candido Junior·Anderson da Silva Soares·Sandra Maria Aluisio·Moacir Antonelli Ponti

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Abstract

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.

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BibTeX RIS

Edresson Casanova, Christopher Shulby, Eren Gölge, Nicolas Michael Müller, Frederico Santos de Oliveira, Arnaldo Candido Junior, Anderson da Silva Soares, Sandra Maria Aluisio, Moacir Antonelli Ponti. 2021-04-02. SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model. https://arxiv.org/abs/2104.05557

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