Discriminatively trained mixture models in continuous speech recognition
US6490555B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 5, 2000 |
| Grant date | Dec 3, 2002 |
| Priority date | — |
| Expiry date | Apr 5, 2020 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/144
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of a continuous speech recognition system is given for discriminatively training hidden Markov for a system recognition vocabulary. An input word phrase is converted into a sequence of representative frames. A correct state sequence alignment with the sequence of representative frames is determined, the correct state sequence alignment corresponding to models of words in the input word phrase. A plurality of incorrect recognition hypotheses is determined representing words in the recognition vocabulary that do not correspond to the input word phrase, each hypothesis being a state sequence based on the word models in the acoustic model database. A correct segment of the correct word model state sequence alignment is selected for discriminative training. A frame segment of frames in the sequence of representative frames is determined that corresponds to the correct segment. An incorrect segment of a state sequence in an incorrect recognition hypothesis is selected, the incorrect segment corresponding to the frame segment. A discriminative adjustment is performed on selected states in the correct segment and the corresponding states in the incorrect segment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.