Professor B: OK So uh today we 're looking at a number of uh things we 're trying and uh fortunately for listeners to this uh we lost some of it 's visual but um got tables in front of us . Um what is what does combo mean ?
PhD C: So combo is um a system where we have these features that go through a network and then this same string of features but low - pass filtered with the low - pass filter used in the MSG features . And so these low - pass filtered goes through M eh another MLP and then the linear output of these two MLP 's are combined just by adding the values and then there is this KLT . Um the output is used as uh features as well .
Professor B: Um so let me try to restate this and see if I have it right . There is uh there is the features uh there 's the OGI features and then um those features um go through a contextual uh l l let 's take this bottom arr one pointed to by the bottom arrow . Um those features go through a contextualized KLT . Then these features also uh get um low - pass filtered
PhD C: Yeah . Yeah so yeah I could perhaps draw this on the blackboard
Professor B: Sure . Yeah . Yeah .
PhD C: Yeah .
PhD D: The graph , yeah another one .
Professor B: Yeah , that 's good .
PhD C :
Professor B: So
PhD C: So we have these features from OGI that goes through the three paths .
Professor B: Yeah . Three , OK .
PhD C: The first is a KLT using several frames of the features .
Professor B: Yeah . Yeah .
PhD C: The second path is uh MLP also using nine frames several frames of features
Professor B: Yeah . Uh - huh .
PhD C: The third path is this low - pass filter .
Professor B: Uh - huh .
PhD C: Uh , MLP
Professor B: Aha ! aha !
PhD C: Adding the outputs just like in the second propose the the proposal from for the first evaluation .
Professor B: Yeah ? Yeah . Yeah .
PhD C: And then the KLT and then the two together again .
Professor B: No , the KLT . And those two together . That 's it .
PhD D: Two HTK .
Professor B: OK so that 's that 's this bottom one .
PhD C: Um . So this is yeah
Professor B: 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 they 're the best ?
PhD C: Yeah that 's the reason , yeah .
Professor B: Oh let 's focus on them then so what 's the block diagram for the one above it ?
PhD C: For the f the f first yellow line you mean ?
Professor B: Yeah .
PhD C: Yeah so it 's uh basically s the same except that we don't have this uh low - pass filtering so we have only two streams .
PhD D: Step .
PhD C: Well . There 's there 's no low low - pass processing used as additional feature stream .
Professor B: Mm - hmm . Mm - hmm .
PhD C: Um
Professor B: 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 all of these numbers .
PhD C: Yeah . Uh actually the way things seems to um well it 's uh forty percent for TI - digit , sixty for all the SpeechDat - Cars , well all these languages . Ehm the well match is forty , medium thirty five and high mismatch twenty - five . Yeah .
Professor B: Um and we don't have the TI - digits part yet ?
PhD C: Uh , no .
Professor B: OK .
PhD C: But yeah . Generally what you observe with TI - digits is that the result are very close whatever the the system .
Professor B: OK . And so have you put all these numbers together into a single number representing that ?
PhD C: Yeah .
Professor B: I mean not PhD C: Uh not yet .
Professor B: OK so that should be pretty easy to do and that would be good PhD C: No . Mmm yeah , yeah .
Professor B: then we could compare the two and say what was better .
PhD C: Mmm . Yeah .
Professor B: Um and how does this compare to the numbers oh so OGI two is just the top top row ?
PhD D: Yeah .
PhD C: 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 they 've they 're still made some changes in the features
Professor B: OK .
PhD C: 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
Professor B: OK .
PhD C: Uh
Professor B: Uh OK so the one one place where it looks like we 're messing things up a bit is in the highly mismatched Italian .
PhD C: Yeah . Yeah .
Professor B: An
PhD C: Yeah there is something funny happening here because yeah .
Professor B: Yeah .
PhD C: But there are thirty - six and then sometimes we are we are we are around forty - two and
Professor B: Now up
PhD C: but
Professor B: Uh so one of the ideas that you had mentioned last time was having a a second um silence detection .
PhD C: Yeah . So there are some results here
PhD D: For the Italian .
PhD C: uh so the third and the fifth line of the table
PhD D: For this one .
Professor B: So filt is what that is ?
PhD C: Filt , yeah
PhD D: Yeah .
PhD C: Um yeah so it seems f for the the well match and mismatched condition it 's uh it brings something . Uh but uh actually apparently there are there 's no room left for any silence detector at the server side because of the delay . Uh well
Professor B: Oh we can't do it . Oh OK .
PhD C: No .
PhD D: For that for that we Professor B: Oh .
PhD C: Uh
Professor B: Too bad . Good idea , but can't do it .
PhD C: Yeah .
Professor B: OK .
PhD C: Except I don't know because they I think they are still working well .
Professor B: Uh - huh .
PhD C: 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 that 's 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 so we don't
Professor B: OK . So Alright so for now at least that 's not there you have some results with low - pass filter cepstrum doesn't have a huge effect but it but it looks like it you know maybe could help in a couple places .
PhD C: I th
Professor B: Uh little bit .
PhD C: Yeah .
Professor B: Um and um um Yeah and uh let 's 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 let 's see that one Well even the just the the second row doesn't look that bad right ? That 's just uh yeah ?
PhD C: Yep .
Professor B: And and that looks like an interesting one too .
PhD D: Mmm yeah .
Professor B: Uh
PhD C: 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 .
Professor B: Mm - hmm .
PhD C: Um
Professor B: Yeah . Yeah so when we do this weighted measure we should compare the two cuz it might even come out better . And it 's it 's it 's a little slightly simpler .
PhD C: Mm - hmm . Yeah .
Professor B: So so there 's so I I would put that one also as a as a maybe . Uh and it yeah and it 's 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 it 's worse than a few things
PhD C: Mm - hmm .
Professor B: so uh let 's see how that c that c c see how that comes out on their their measure and are are we running this uh for TI - digits or uh
PhD C: Yeah .
Professor B: Now is TI di is is that part of the result that they get for the uh development th the results that they 're supposed to get at the end of end of the month , the TI - digits are there also ?
PhD C: Yeah . Yeah . It 's included , yeah .
Professor B: Oh OK . OK . And see what else there is here . Um Oh I see the one I was looking down here at the the o the row below the lower yellowed one . Uh that 's uh that 's with the reduced uh KLT size reduced dimensionality .
PhD C: Mm - hmm ? Yeah . Yeah .

Summary: The team used OGI features that then passed through a contextualized KLT, an MLP, and a low-pass filter. The highly mismatched Italian part was still not working well. The team tried using silence detection to improve performance, but the results were not too promising.