Lattice encoding using recurrent neural networks
US10176802B1 · kind B1 · utility
148Cited by
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20Claims
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Key dates
| Filing date | Apr 6, 2016 |
| Grant date | Jan 8, 2019 |
| Priority date | — |
| Expiry date | Sep 12, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An automatic speech recognition (ASR) system may convert an ASR output lattice into a matrix form, thus maintaining certain information included in the lattice that might otherwise be lost in an N-best list output. The matrix representation of the lattice may be encoded using a recurrent neural network (RNN) to create a vector representation of the lattice. The vector representation may then be used by the system to perform additional operations, such as ASR results confirmation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.