Waveform generation using end-to-end text-to-waveform system
US11482207B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 21, 2020 |
| Grant date | Oct 25, 2022 |
| Priority date | — |
| Expiry date | Dec 21, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L13/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described herein are embodiments of an end-to-end text-to-speech (TTS) system with parallel wave generation. In one or more embodiments, a Gaussian inverse autoregressive flow is distilled from an autoregressive WaveNet by minimizing a novel regularized Kullback-Leibler (KL) divergence between their highly-peaked output distributions. Embodiments of the methodology computes the KL divergence in a closed-form, which simplifies the training process and provides very efficient distillation. Embodiments of a novel text-to-wave neural architecture for speech synthesis are also described, which are fully convolutional and enable fast end-to-end training from scratch. These embodiments significantly outperform the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet. Also, a parallel waveform synthesizer embodiment conditioned on the hidden representation in an embodiment of this end-to-end model were successfully distilled.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.