Yeah Or I could just add them up and get a different total Yeah not the square OK Uh What else wh what else is going on Uh yeah Yeah at first I had a remark why I am wondering why the PDA is always so far I mean we are always meeting at the beginning of the table and the Uh there guess cuz we haven't wanted to move it We we could Yeah we could move us and OK right Well anyway Um Yeah so Uh Since the last meeting tried to put together um the clean um downsampling upsampling I mean Uh the new filter replacing the LDA filters and also the um delay issue so that We considered th the the delay issue on the for the normalization Mmm So put together all this and then we have results that are not um very impressive Well there is no real improvement But not wer worse and not better better latency right Yeah Yeah Well Actually better It seems better when we look at the mismatched case but I think we are like like cheated here by the th this problem that uh in some cases when you modify slight slightly modify the initial condition you end up completely somewhere air somewhere else in the in the space the Yeah parameters So Well The other system are for instance For Italian is at percent recognition rate on the mismatch and this new system has But I don't think it indicates something really I don't I don't think it means that the new system is more robust or simply the fact that Well Well the test Y be if you then tried it on one of the other test sets if if it was Right So this was Italian right Yeah Yeah So then if you take similar changes for and other then test sets but I mean from this se um percent recognition rate system I could change the transition probabilities for the the first HMM and it will end up to also By using point five instead of point six point four as in the the HTK script Yeah So Well Yeah Yeah I looked Well at um Eh uh looked at the results when Stephane did that and really wo really happens This really happens mean Yeah th the only difference is you change the transition probability by a tenth of a percent and it causes ten percent difference in the A word tenth error of rate cent Even Yeah tenth From percent point Well we tried sorry f we for tried point from point one change at point one Hmm Oh and n not tenth of a percent one tenth alright Yeah Um so from point five so from point six to point five and you get ten percent better And I think what you basically hypothesized in the last meeting about uh it just being very and I think you mentioned this in your email Mmm too yeah just very um you know get stuck in some local minimum and this thing throws you out of it I guess Well what are according to the rules what what are we supposed to do about the transition probabilities Are they supposed to be point five or point six think not allowed to Yeah Yeah supposed to be point six for the Point supposed to be point six Yeah But changing it to point five I think is which gives you much better results but not allowed But not allowed Yeah Yeah OK Yeah but even if you use point five not sure it will always give you the better results on other Yeah test set or it Right We only the tested training on set the mean the medium mismatch right Yeah You said on the other cases you didn't notice yeah I think the reason is yeah I not I it was in my mail I think also is the fact that the mismatch is trained only on the far microphone Well in for the mismatched case everything is um using the far microphone training and testing whereas for the highly mismatched training is done on the close microphone so clean speech basically so you don't have this problem of local minima probably and for the a mix of close microphone and distant microphone and Well I did notice th uh I something mismatch is the more difficult for the training part Somebody I think it was Morgan suggested at the last meeting that I actually count to see how many parameters and how many frames And there are uh almost one point eight million frames of training data and less than forty thousand parameters in the baseline system Hmm Yeah So very very few parameters compared to how much training data Well Yes So And that that says that we could have lots more parameters Yeah actually Yeah I did one quick experiment just to make sure I had everything worked out and I just uh f for most of the um For for all of the digit models they end up at three mixtures per state And so I just did a quick experiment where I changed it so it went to four and um it it it didn't have a r any significant effect at the uh medium mismatch and high mismatch cases and it had it was just barely significant for the better Uh so r gonna run that again but um with many more uh mixtures per state Yeah Cuz at forty thou I mean you could you could have uh Yeah easily four times as many parameters And I think also just seeing what we saw uh in terms of the expected duration of the silence model when we did this tweaking of the Yeah The silence model expected duration was really different And so Yeah case where um it had a better score the silence model expected duration was much longer So it was like it was a better match I think you know if we make a better silence model I think that will help a lot too um for a lot of these cases so but one one thing I I wanted to check out before I increased the um number of mixtures per state was uh in their default training script they do an initial set of three and then they built the silence model and then they do seven iterations then the add mixtures and they do another seven then they add mixtures then they do a final set of seven and they quit Seven seems like a lot to me and it also makes the experiments go take a really long time I mean to do one of the well matched case takes like a day And so you know in trying to run these experiments I notice you know difficult to find machines you know compute the run on And so one of the things I did was I compiled HTK for the Linux machines cuz we have this one from IBM got like five processors in it Right