Once things are basically working, how do we *improve* accuracy?
Solution: Basically you need to take a representative sample of test data, and try to find out why mis-recognition took place. It’s time consuming but you reap the rewards later. When doing this, it might become obvious, for example, that certain words are regularly being mis-recognised, and that’s a good time to check what the pronunciation should be (according to the dictionary). Where it’s not a good match, simply add a new pronunciation of that word (without deleting the original). If a word is misrecognized only at a certain location in the grammar, (e.g., FOUR at the end of the utterance but not elsewhere), then you can adjust the weights on lattice links to make that selection more or less likely. But try that as a last resort; the system should be globally optimized before doing something this fussy. You can do adaptation, too. We are confident we can get the error rate to a very low level with adaptation (the results with large vocabulary are quite astonishing), but perhaps no
