well , it  that  that could be th at the server 's side ,
It could be ,
and , well. Mmm , yeah.
but that 's the image they have , right ? So that 's  that 's  I mean , one could argue all over the place about how things really will be in ten years. But the particular image that the cellular industry has right now is that it 's distributed speech recognition , where the , uh , uh , probabilistic part , and  and s semantics and so forth are all on the servers , and you compute features of the  uh , on the phone. So that 's  that 's what we 're involved in. We might  might or might not agree that that 's the way it will be in ten years , but that 's  that 's  that 's what they 're asking for. So  so I think that  th th it is an important issue whether it works cross - language. Now , it 's the OGI , uh , folks ' perspective right now that probably that 's not the biggest deal. And that the biggest deal is the , um envir acoustic - environment mismatch. And they may very well be right , but I  I was hoping we could just do a test and determine if that was true. If that 's true , we don't need to worry so much. Maybe  maybe we have a couple languages in the training set and that gives us enough breadth uh , uh , that  that  that the rest doesn't matter. Um , the other thing is , uh , this notion of training to uh  which I  I guess they 're starting to look at up there ,  training to something more like articulatory features. Uh , and if you have something that 's just good for distinguishing different articulatory features that should just be good across , you know , a wide range of languages.