Deep convex network with joint use of nonlinear random projection, restricted boltzmann machine and batch-based parallelizable optimization
US9390371B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 17, 2013 |
| Grant date | Jul 12, 2016 |
| Priority date | — |
| Expiry date | Jan 6, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/16
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method is disclosed herein that includes an act of causing a processor to access a deep-structured, layered or hierarchical model, called a deep convex network, retained in a computer-readable medium, wherein the deep-structured model comprises a plurality of layers with weights assigned thereto. This layered model can produce the output serving as the scores to combine with transition probabilities between states in a hidden Markov model and language model scores to form a full speech recognizer. Batch-based, convex optimization is performed to learn a portion of the deep convex network's weights, rendering it appropriate for parallel computation to accomplish the training. The method can further include the act of jointly substantially optimizing the weights, the transition probabilities, and the language model scores of the deep-structured model using the optimization criterion based on a sequence rather than a set of unrelated frames.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.