Mm - hmm.
And so they had sixteen. So that 's pretty close. Um   but for the  most of the words are sh much shorter. So the majority of them wanna have nine states. And so theirs are s sort of twice as long. So  my guess  uh And then if you  I  I printed out a confusion matrix um  uh for the well - matched case , and it turns out that the longest words are actually the ones that do the best. So my guess about what 's happening is that  you know , if you assume a fixed  the same amount of training data for each of these digits and a fixed length model for all of them but the actual words for some of them are half as long you really um have , you know , half as much training data for those models. Because if you have a long word and you 're training it to eighteen states ,   uh you 've got  you know , you 've got the same number of Gaussians , you 've gotta train in each case ,
Mm - hmm.
but for the shorter words , you know , the total number of frames is actually half as many.