Parallel neural text-to-speech
US11017761B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 16, 2019 |
| Grant date | May 25, 2021 |
| Priority date | — |
| Expiry date | Nov 6, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Presented herein are embodiments of a non-autoregressive sequence-to-sequence model that converts text to an audio representation. Embodiment are fully convolutional, and a tested embodiment obtained about 46.7 times speed-up over a prior model at synthesis while maintaining comparable speech quality using a WaveNet vocoder. Interestingly, a tested embodiment also has fewer attention errors than the autoregressive model on challenging test sentences. In one or more embodiments, the first fully parallel neural text-to-speech system was built by applying the inverse autoregressive flow (IAF) as the parallel neural vocoder. System embodiments can synthesize speech from text through a single feed-forward pass. Also disclosed herein are embodiments of a novel approach to train the IAF from scratch as a generative model for raw waveform, which avoids the need for distillation from a separately trained WaveNet.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.