Discriminative training of language models for text and speech classification
US7379867B2 · kind B2 · utility
25Cited by
2References
13Claims
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Key dates
| Filing date | Jun 3, 2003 |
| Grant date | May 27, 2008 |
| Priority date | — |
| Expiry date | Jan 9, 2026 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/183
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods are disclosed for estimating language models such that the conditional likelihood of a class given a word string, which is very well correlated with classification accuracy, is maximized. The methods comprise tuning statistical language model parameters jointly for all classes such that a classifier discriminates between the correct class and the incorrect ones for a given training sentence or utterance. Specific embodiments of the present invention pertain to implementation of the rational function growth transform in the context of a discriminative training technique for n-gram classifiers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.