OK Mike Ah on Yes please I mean testing noise robustness but not get silly OK so uh got some uh Xerox things to pass out That Yeah Yeah um Yeah Yeah sorry for the table but as it grows in size uh it Uh so for th the last column we use our imagination OK Ah yeah Ah Uh yeah Uh This you nice though This has nice big font Yeah see Yeah Yeah Chop When So get older you have these different perspectives I mean lowering the word hour rate is fine but having big font Next time we will put colors or something Yeah Uh mostly big font OK Uh s Go so ahead there is kind of summary of what has been done this Oh Summary OK of experiments since well since last week and also since the started to run work on this Um So since last week started to fill the column with um uh features w with nets trained on PLP with normalization but with delta also because the column was not completely well still not completely filled but we have more results to compare with network using without PLP and finally um ehhh PL uh delta seems very important Uh I don't know If you take um say anyway so the next t the second uh part of the table uh when we use the large training set using French Spanish and English you have one hundred and six without delta and with the delta a And again all of these numbers are with a hundred percent being Yeah the on baseline yeah performance but with a So cepstra Yeah system going straight into the HTK Yes So now we see that the gap between the different training set is much uh uh much smaller out um But actually um for English training on TIMIT is still better than the other languages And Mmm Yeah And f also for Italian actually If you take the second set of experiment for Italian so the mismatched condition um when we use the training on TIMIT so we have a number and training with other languages is a little bit worse Um Oh I see Down near the bottom of this sheet Uh So yeah yes OK And yeah and here the gap is still more important between using delta and not using delta Yes y if I take the training s the large training set we have one hundred and Yeah and one hundred and four when we use delta Uh Even if the contexts used is quite the same because without delta we use seventeenths seventeen frames Uh Yeah um so the second point is that we have no single experiments uh that we did not have last week Uh so this is training the net on French only or on English only and testing on Italian And training the net on French only and Spanish only and testing on uh And fff um yeah What we see is that these nets are not as good except for the which is always one of the best Yeah then we started to work on a large dat database containing uh sentences from the French from the Spanish from the TIMIT from SPINE uh from uh English digits and from Italian digits So this is the another line another set of lines in the table Ah yes with SPINE and uh actually we did this before knowing the result of all the data uh so we have to to redo the uh the experiment training the net with uh PLP but with delta But um this this net performed quite well Well better than the net using French Spanish and English only Uh So uh yeah We have also started feature combination experiments Uh many experiments using features and net outputs together And this is The results are on the other document Uh we can discuss this after perhaps well just Yeah so basically there are four four kind of systems The first one yeah is combining um two feature streams uh using and each feature stream has its own MPL So the kind of similar to the tandem that was proposed for the first The tandem for the first proposal The second is using features and KLT transformed MLP outputs And the third one is to u use a single KLT trans transform features as well as MLP outputs Um yeah Mmm You know you can you can comment these results Yes I can s I would like to say that for example um mmm if we doesn't use the uh we have an improve when we use s some combination But Yeah when ju w just to be clear the numbers here are Yeah uh recognition accuracy Yeah So this number recognition Again acc we switch to another Yes and the baseline the baseline have i is Baseline is Yeah So experiment only on the Italian mismatched for the moment for this Uh Um is Italian mismatched OK And first in the I I do I I use different MLP and is obviously that the MLP is the better Um for the ne rest of experiment I use only And I try to combine different type of feature but the result is that the feature doesn't work for the Italian database because never help to increase the accuracy Yeah eh actually if w we look at the table the huge table um we see that for MSG perform as well as the PLP but this is not the case for Italian what where the error rate is c is almost uh twice the error rate of PLP So um uh well I don't think this is a bug but this this is something in probably in the MSG um process that uh I don't know what exactly Perhaps the fact that the the no filter well or no filter and that there is some DC offset in the Italian or well something simple like that But that we need to sort out if want to uh get improvement by combining PLP and MSG because for the moment MSG do doesn't bring much information And as I Carmen said if we combine the two we have the result basically of PLP Um the uh baseline system when you said the baseline system was uh uh percent that was trained on what and tested on what That was uh Italian mismatched d uh Yeah uh digits uh is the testing and the training is Italian digits Yeah So the mismatch just refers to the noise and and uh microphone and so forth Yeah right Yeah So um did we have So would that then correspond to the first line here of where the training is is the uh Italian digits The train the training of the HTK Yes Yes yes This h Yes Yes Th Yes Training of the net yeah Yeah