Can I read Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis on EtoBox?

Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis by Weiss, Ron J.; Skerry-Ryan, RJ; Battenberg, Eric; Mariooryad, Soroosh; Kingma, Diederik P. is a scholarly article available to read on EtoBox.

What is Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis about?

We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating a normalizing flow into the autoregressive decoder loop. Output waveforms are modeled as a sequence of non-overlapping fixed-length blocks, each one containing hundreds of samples. The interdependencies of waveform samples within each block are modeled using the normalizing flow, enabling parallel training and synthesis. Longer-term dependencies are handled autoregressively by conditioning each flow on preceding blocks.This model can be optimized directly with maximum likelihood, with-out using intermediate, hand-designed features nor additional loss terms. Contemporary state-of-the-art text-to-speech (TTS) systems use a cascade of separately learned models: one (such as Tacotron) which generates intermediate features (such as spectrograms) from text, followed by a vocoder (such as WaveRNN) which generates waveform samples from the intermediate features. The proposed system, in contrast, does not use a fixed intermediate representation, and learns all parameters end-to-end. Experiments show that the proposed model

Author

Weiss, Ron J.; Skerry-Ryan, RJ; Battenberg, Eric; Mariooryad, Soroosh; Kingma, Diederik P.

Published

2020

Language

EN