In a hidden Markov model there are certain assumptions about the data that comes in, some of which are not that accurate. So for example, there's a conditional -- this is going to get too technical -- but yes, there are some challenges in modeling longer-distance constraints. That's an active research area. How do we alter the model so that we can do a better job for those longer-distance restraints that matter, to capture them in a model? For example, we have something called delta feature, so we not only look at what's the acoustics at this moment, but what's the trajectory of those acoustics? Is this part of it rising, falling or whatever?
So that tells us something about what's happening at longer distance, even within these constraints of the assumptions about the statistics of what we're able to model with that kind of a model.