need to be But first first of course you would wanna process the training data because Right wanna get that started Yeah Yeah I mean it be really great if it was all automatic but I think that you know given the pressure of time if i i I mean since gonna find out in a short amount of time great But i if if it doesn't work out I think we would rather charge ahead with the older segmentations Right and Um and we were gonna use one of the I don I don't know what Probably whatever one been using for I think for that one for Right the digits Is it this one e F F Yeah it which F How F do you know the second nearest the machine room Oh Bien sur OK Alright so Bet have fun with that one OK so Uh OK So uh I just want to make sure I understand what we need to run Um see So OK so if talking about assume that gonna use the new segmentations We need to um run recognition o just looking at the no overlap column Basically we have to d r do recognitions for all three of those cases Right Right Um because gonna be using the just the male uh model short training set for the male So we need to have results for all three of those even Ma we maybe you should limit ourselves to the Meeting Recorder meetings OK Um if you were gonna cut down on the test set I would suggest Well that Maybe But I I mean how long does it take Actually the for longer test Robustness meetings take longer because this one speaker who talks a lot And so the Um No because for all the for the adaptation and normalization steps you cannot you have to d you have to uh um you cannot chop it up into small pieces So sort of limited by how long the longest speaker uh is s speaking So how much data there is from the the speaker who talks the most So um you parallelize across different speakers but you know if you Right bunch of speakers who speak very little and then one wh who who speaks a lot then effectively Right everybody waits for the longest one Yeah to process But So what what was your result for uh that we had at the HLT Was that a combination was of both types of meetings but most but there were only two Robustness meetings and four or five And if uh the baseline Recorders it it would be OK Right I mean Right To to just limit ourselves to a smaller How long would it take to run recognition if we did that Oh Uh I I I don't have I mean don't is have is good like a gue day or is it a few hours or roughly For everything For all the meetings For yeah say we just Uh Meeting Recorder meetings for our test set Um probably more than a day but probably less than two Oh really I didn't realize each test took that long Well i No I mean for all the meetings Because Again um So you So were each doing meeting each meetings takes uh something like Again we we I ran when we ran these we were sort of short on machines and um I don't know I I would estimate maybe four hours per meeting Something like that Four hours per meeting Right Wow Yeah But if you So if you do half a dozen meetings about a day We Right also have more machines now well Right So why saying not sure how they would Yeah scale with more machines Yeah I mean if we had about six A six hour test not bad Right Right S six hour test set Six know meetings OK Right I mean a lot of the Yeah evaluations have been We have MR two th We have two three four fi I think there are four Meeting Recorder meetings that we worked with Four that you worked think with Is it the same set as Yeah alignments I think five Five Meeting Recorder meetings OK That would be OK too I mean i So if they have a set that they worked with and you you got Did you do similarly in performance between them and the other meetings or was it Uh with the Robust compared to Robustness Yeah The big variation is by whether a native speaker Yeah or not And whether um Uh I think the o actually and and of course what um you know whether lapel or uh headset microphone And overlap or not Yeah So maybe just with the the the Meeting Recorder set of the And we can De exclude th we that don't you need did to before recognize the because we know that I mean in fact we excluded them previously Yeah So from to do the same Right same thing OK So Alright So if we got a list of the uh segmentations for these five Meeting Recorder meetings we could start uh the first two experiments going right away using the short male models So you could get those going while Dave is um creating waveforms Yeah for the r retraining the short male models Once we know which segmentations using Yeah Right OK OK And then OK So do we w also want to run that bottom experiment without retraining the short male models on his thing Did you want that Or Um I I agree that that would be an interesting thing to do but I sort of regard it as secondary OK So So if save sort that machines sitting around and people sitting around and waiting for other things to finish then sure But Uh Chuck had been asking about that earlier as kind of a control to know um Cuz I mean you could imagine a fantasy in which you said that processing made the uh far microphone like the near microphone In which case you shouldn't have to actually retrain But not really true sort of fantasy It does it does muck up the data in in some funny ways And so Well i it kind of questioning that But But Well on a more basic level also it means that that third experiment there are actually two differences between the other experiments not one Right So hard to know It involves retraining and it involves a Right Uh right I mean the other thing which which it might come in to is if there was some problem in the retraining I mean maybe just have some mechanical thing we do wrong Uh Right that uh since experience was that it didn't help as much if you didn't retrain but it does help some that we would hopefully see that So that true Wait I