Model shrinking for embedded keyword spotting
US9600231B1 · kind B1 · utility
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20Claims
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Key dates
| Filing date | Jun 26, 2015 |
| Grant date | Mar 21, 2017 |
| Priority date | — |
| Expiry date | Jun 26, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/223
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A revised support vector machine (SVM) classifier is offered to distinguish between true keywords and false positives based on output from a keyword spotting component of a speech recognition system. The SVM operates on a reduced set of feature dimensions, where the feature dimensions are selected based on their ability to distinguish between true keywords and false positives. Further, support vectors pairs are merged to create a reduced set of re-weighted support vectors. These techniques result in an SVM that may be operated using reduced computing resources, thus improving system performance.
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