back to NEC
I went back to the machines and the data at NEC, because I was revising the ICME paper. The re-ripping process ended up with a slightly different data set than what's at NEC, so I wanted to stick with that. Also those machines rock.
I moved the ALA code over there, and added some code to evaluate-responses to handle ALA SIM matrices, and ran the eval. Turns out that ALA does worse than centroids! The numbers are all in the . revised ICME paper. I suspect that it's because the distributions are actually pretty unimodal, as I've seen making the anchorspace visualization slides. So maybe training GMMs isn't the thing to do after all. Why the distributions should be so single Gaussian-like is a mystery at this point.
Then I did what we should have done the first time: trained models on the cepstra directly. They did worse than the anchor models, and even a little worse than the random anchors. So that's good news. But it's not quite a fair test: the anchor models have 14-dimensions, and the cepstra models have 20. So I'm in the middle of reducing the 20 cepstra dimensions to 14 via PCA, and then I'll train models on that and evaluate.