The technique you're thinking of is called oversampling and there are many other general techniques for dealing with imbalanced datasets, as it's a very common situation.
The model itself has generalized pretty well to handle both single and multi word utterances I think, without a separate classifier, but I'm definitely not going to rule out multi-model recognition in the long run.
My main issues with single words right now are:
- The model sometimes plays favorites with numbers (ace vs eight)
- Collecting enough word-granularity training data for words-that-are-not-numbers (I've done a decent job of this so far, but it's a slow and painful process. I've considered building a frontend to turn sentence datasets into word datasets with careful alignment)
An issue to watch for though is elision: a word in a sentence can often be said differently to the individual words, eg saying "last" and "time" separately one typically includes the final t in last and yet said together, commonly it's more like "las time".
I think I'd be very cautious about it and use a model with a different architecture than the aligner to validate extracted words, and probably play with training on the data a bit to see if the resulting model makes sense or not. I do have examples of most english words to compare extracted words.