Low-footprint adaptation and personalization for a deep neural network
US9324321B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 7, 2014 |
| Grant date | Apr 26, 2016 |
| Priority date | — |
| Expiry date | Mar 7, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/075
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The adaptation and personalization of a deep neural network (DNN) model for automatic speech recognition is provided. An utterance which includes speech features for one or more speakers may be received in ASR tasks such as voice search or short message dictation. A decomposition approach may then be applied to an original matrix in the DNN model. In response to applying the decomposition approach, the original matrix may be converted into multiple new matrices which are smaller than the original matrix. A square matrix may then be added to the new matrices. Speaker-specific parameters may then be stored in the square matrix. The DNN model may then be adapted by updating the square matrix. This process may be applied to all of a number of original matrices in the DNN model. The adapted DNN model may include a reduced number of parameters than those received in the original DNN model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.