OK So uh today looking at a number of uh things trying and uh fortunately for listeners to this uh we lost some of visual but um got tables in front of us Um what is what does combo mean So combo is um a system where we have these features that go through a network and then this same string of features but filtered with the filter used in the MSG features And so these filtered goes through M eh another MLP and then the linear output of these two are combined just by adding the values and then there is this KLT Um the output is used as uh features as well Um so let me try to restate this and see if I have it right There is uh there is the features uh the OGI features and then um those features um go through a contextual uh l l take this bottom arr one pointed to by the bottom arrow Um those features go through a contextualized KLT Yeah Then these features also uh get um filtered Yeah so yeah I could perhaps draw this on Sure blackboard Yeah Yeah Yeah The graph yeah another one Yeah good So So have these features from OGI Yeah that goes through the three paths Three OK The first is a KLT using several frames Yeah of the features Yeah The second path is uh MLP Yeah also using nine frames several frames of features The third path is this filter Uh MLP Aha aha Adding the outputs just like in the Yeah second propose the the proposal from for Yeah first evaluation Yeah And then No the KLT the KLT And those two together and then the two together again Two HTK Um OK so this bottom So one this is yeah And so uh and then the the the one at the top and I presume these things that uh are in yellow are in yellow because overall the best Yeah the reason yeah Oh focus on them then so the block diagram for the one above it For the f the f first yellow line you mean Yeah Yeah so uh basically s the same except that we don't have this Step uh filtering so we have only two streams Well no low processing used as additional feature stream Um Do you e um they mentioned made some uh when I was on the phone with Sunil they they mentioned some weighting scheme that was used to evaluate Yeah all of these numbers the way things seems to um well uh forty percent for sixty for all the well all these languages Ehm the well match is forty medium thirty five and high mismatch Yeah Um and we don't have the part yet Uh no OK But yeah Generally what you observe with is that the result are very close whatever the the system OK And so Yeah have you put all these numbers together into a single number representing that I mean not Uh not yet No OK so that should be pretty easy to Mmm do yeah and that would be good then we could compare Mmm what was better Yeah Um and how does this compare to the numbers oh so OGI two is just the top Yeah top row So yeah to actually OGI two is the the baseline with the OGI features but this is not exactly the result that they have because still made some changes in the features OK and well but uh actually our results are better than their results Um I don't know by how much because they did not send us the new results OK Uh OK so the one one place where it looks like messing things up a bit is in the highly mismatched Italian Yeah Yeah An there is something funny happening here because yeah Yeah But there are and then sometimes we are we are we are around and Now but up Uh so one of the ideas that you had mentioned last time was having a a second um silence detection Yeah So there are some results here For the Italian uh this so the third and the fifth line of the table So filt is Filt is Yeah Um yeah so it seems f for the the well match and mismatched condition uh it brings something Uh but uh actually apparently there are no room left for any silence detector at the server side because of the delay Uh Oh well we can't do it Oh OK No For that for that Oh we Uh bad Good idea but Yeah can't do it OK Except I don't know because they I think they are still working well Uh t two days ago they were still working on this trying to reduce the delay of the silence detector so but yeah if we had time perhaps we could try to find uh some kind of compromise between the delay on the handset and on the server side Perhaps try to reduce the delay on the handset and but well hmm For the moment they have this large delay on the the feature computation and OK So so Alright so for now at least not there you have some results with filter cepstrum doesn't have a huge effect but it but it looks like it you know maybe could th places Yeah Uh little bit Um and um um Yeah and uh see What else did we have in there Uh I guess it makes a l um at this point this is I I guess I should probably look at these others a little bit uh And you you yellowed these out uh but uh uh Oh I see yeah that that one you can't use because of the delay Those look pretty good Um see that one Well even the just the the second row doesn't look that bad right just uh Yep yeah And yeah and that looks like an interesting one too Uh Actually the yeah the second line is uh pretty much like the first line in yellow except that we don't have this KLT on the first on the left part of the diagram We just have the features as they are Um Yeah Yeah so when we do this weighted measure we should compare the two cuz it might even come out better And a little slightly simpler Yeah So so so I I would put that one also as a as a maybe Uh and it yeah and actually does does significantly better on the uh uh highly mismatched Italian so s and little worse on the mis on the MM case but uh Well yeah worse than a few things so uh see how that c that c c see how that comes out on their their measure and are are we running this uh for or uh Yeah Yeah