Mm - hmm.
and just looked at , you know , the number of phones in each one of the digits. Um you know , sort of the canonical way of setting up a  an HMM system is that you use  um three states per phone and um  so then the  the total number of states for a word would just be , you know , the number of phones times three. And so when I did that for the Italian digits , I got a number of states , ranging on the low end from nine to the high end , eighteen. Um.  Now you have to really add two to that because in HTK there 's an initial null and a final null so when they use  uh models that have eighteen states , there 're really sixteen states. They 've got those initial and final null states. And so um   their guess of eighteen states seems to be pretty well matched to the two longest words of the Italian digits , the four and five  which um , according to my , you know , sort of off the cuff calculation , should have eighteen states each.
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 ,