Deep belief network for large vocabulary continuous speech recognition
US8972253B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 15, 2010 |
| Grant date | Mar 3, 2015 |
| Priority date | — |
| Expiry date | Apr 29, 2032 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method is disclosed herein that includes an act of causing a processor to receive a sample, wherein the sample is one of spoken utterance, an online handwriting sample, or a moving image sample. The method also comprises the act of causing the processor to decode the sample based at least in part upon an output of a combination of a deep structure and a context-dependent Hidden Markov Model (HMM), wherein the deep structure is configured to output a posterior probability of a context-dependent unit. The deep structure is a Deep Belief Network consisting of many layers of nonlinear units with connecting weights between layers trained by a pretraining step followed by a fine-tuning step.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.