going OK OK Close your Thanks on door on the way out Thanks Oh Yeah Probably wanna get this other door too OK So Um What are we talking about today Uh well first there are perhaps these uh Meeting Recorder digits Oh yeah That was that kind tested uh So interesting The both Um the uh the SRI System and the oth And for one thing that that sure shows the difference between having a lot of uh Of data training data Yeah or not uh the uh The best kind of number we have on the English uh on near microphone only is is uh three or four percent And uh significantly better than that using fairly simple on on the uh uh with the SRI system So I th I think that the uh But using uh a a pretty huge amount of data mostly not digits of course but but then again Well yeah In fact mostly not digits for the actual training the whereas uh in this case just using digits for training the Yeah Right Did anybody mention about whether the the SRI system is a is is doing the digits um the wor as a word model or as uh a sub s I guess states uh allophone models Yeah well Probably Huh Yeah I think so because their very d huge their huge system Yeah And But So There is one difference Well the SRI system the result for the SRI system that are represented here are with adaptation So there is their complete system and including uh unsupervised adaptation true And if you don't use adaptation the error rate is around fifty percent worse I think if I remember Yeah OK tha that Nnn much huh Yeah quite significant Yeah Oh OK Still But but uh what what I think be interested to do given that is that we we should uh take I guess that gonna do this right is to take some of these tandem things and feed it into the SRI system right Yeah Yeah We can do something like that Yeah Yeah Because But I guess the main point is the data because uh I am not sure Our is is fairly simple but until now well the attempts to improve it or have fail Ah well I mean uh what Chuck tried to to to do Yeah but doing it with the same data right Yeah mean so So to Yeah So two things being affected I mean One is that that you know something simple wrong with the been playing a number of states uh I I don't know if he got to the point of playing with the uh number of Gaussians yet but but uh uh you know But yeah so far he hadn't gotten any big improvement but all with the same amount of data which is pretty small Yeah Mmm And um So yeah we could retrain some of these tandem on Well on could huge that but saying even with it not with that part not retrained Ah yeah just Just just using having the f for the much better HMM models Yeah Yeah Um But just train those using different features the features coming Yeah from our Aurora stuff So Yeah But what would be interesting to see also is what what perhaps not related the amount of data but the um recording conditions I don't know Because probably not a problem of noise because our features are supposed to be robust to noise Well not yeah problem of channel because there is um normalization with respect to the channel So I I sorry What what is the problem that trying to explain The the fact that the That result with the tandem and Aurora system are So uh so much worse Yeah Oh I uh but almost certain that it it I mean that it has to do with the um amount of training data It Yeah but orders of magnitude off Yeah but we train only on digits and a digit task so Well But but having a huge If if you look at what commercial places do they use a huge amount of data Alright This is a modest amount of data Yeah So I mean ordinarily you would say well given that you have enough occurrences of the digits you can just train with digits rather than with you know But the thing is if you have a huge in other words do word models But if you have a huge amount of Right data Mmm then going to have many occurrences of similar uh allophones Yeah And just a huge amount of training for it So um I I think it has to be that because as you say this is you know this is really pretty clean data Um Now some of it could be the fact that uh see in the in these things did we include noisy data in the Yeah training I mean that could be hurting us actually for the clean Yeah case Well actually we see that the clean train for the Aurora proposals are It is are if better than the yeah Yeah Yeah Cuz this is clean data and so not too surprising But um Uh So what I meant is that well say if we if we add enough data to train on the um on the Meeting Recorder digits I guess we could have better results than this And What I meant is that perhaps we can learn something uh from this wrong uh what what is different between and these digits and What kind of numbers are we getting on point eight percent so Oh I see Four Fourier So in the actual database getting point eight percent Yeah and here getting three or four three see three for this Yeah Sure but I mean um point eight percent is something like double uh or triple what people have gotten worked very hard at doing that And and also as you point out adaptation in these numbers also Mmm So if you you know put the ad adap take the adaptation off then it for the you get something like two percent And here you had you know something like three point four And I could easily see that difference coming from this huge amount of data that it was trained on So You know Yeah think anything magical here you know we used a simple HTK system with a modest amount of data And this is a a you know modern uh system Yeah uh has has a lot of nice points to it Um So I mean the HTK is an older HTK even So Yeah it not that surprising But to me it just it just meant a practical point that um if we want to publish