Hyper-structure recurrent neural networks for text-to-speech
US10127901B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 13, 2014 |
| Grant date | Nov 13, 2018 |
| Priority date | — |
| Expiry date | Jun 13, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L13/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The technology relates to converting text to speech utilizing recurrent neural networks (RNNs). The recurrent neural networks may be implemented as multiple modules for determining properties of the text. In embodiments, a part-of-speech RNN module, letter-to-sound RNN module, a linguistic prosody tagger RNN module, and a context awareness and semantic mining RNN module may all be utilized. The properties from the RNN modules are processed by a hyper-structure RNN module that determine the phonetic properties of the input text based on the outputs of the other RNN modules. The hyper-structure RNN module may generate a generation sequence that is capable of being converting to audible speech by a speech synthesizer. The generation sequence may also be optimized by a global optimization module prior to being synthesized into audible speech.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.