And then , once you get that done , you  you  you train the whole  whole thing on maximum likelihood. Um , and h he shows that using this  this method to detect sonorance is it 's very robust compared to , um   to typical , uh , full - band Gaussian mixtures um estimations of  of sonorance.
Mm - hmm. Mm - hmm.
And , uh so  so that 's just  that 's just one detector. So you can imagine building many of these detectors on different features. You get enough of these detectors together , um , then you have enough information to do , um , higher level discrimination , for example , discriminating between phones
Mm - hmm.