that I hope that this is going to be a adopted Next Wednesday we are going to have uh another uh teleconferencing call so see what uh where it goes So what about the issue of um the weights on the for the different systems the and Yeah and what a g very good uh point because David says well you know we ca we can manipulate this number by choosing the right weights anyways So while you are right but uh you know but Uh yeah if of course if you put a zero uh weight zero on a mismatched condition or highly mismatched then then you are done But weights were also deter already decided uh half a year ago And the So staying the same Well of course people will not like it Now What is happening now is that I th I think that people try to match the criterion to solution They have solution Now they Right want to make sure their criterion is And I think that this is not the right way Yeah Uh it may be that that Eventually it may ha may ha it may have to happen But should happen at a point where everybody feels comfortable that we did all what we could And I don't think we did Basically I think that that this test was a little bit bogus because of the data and uh essentially there were these arbitrary decisions made and and everything So so so this is so this is where it is So what we are doing at OGI now is uh uh uh working basically on our parts which we I think a little bit neglected like noise separation Uh so we are looking in ways is in uh which uh with which we can provide better initial estimate of the mel spectrum basically which would be a l uh f more robust to noise and so far not much uh success Hmm We tried uh things which uh a long time ago Bill Byrne suggested instead of using Fourier spectrum from Fourier transform use the spectrum from LPC model Their argument there was the LPC model fits the peaks of the spectrum so it may be m naturally more robust in noise And I thought well that makes sense but so far we can't get much Hmm much out of it uh we may try some standard techniques like spectral subtraction and haven't tried that yet not not not much Hmm Or even I was thinking about uh looking back into these totally techniques like for instance uh Dennis Klatt was suggesting uh the one way to uh deal with noisy speech is to add noise to everything Hmm So I mean uh uh add moderate amount of noise Oh all data I see that makes uh th any additive noise less addi less a a effective right Right Because you already uh had the noise uh in a And it was working at the time It was kind of like one of these things you know but if you think about it actually pretty ingenious So well you know Well just basically take y spectrum and and and add of the constant C to every every value Yeah So making all your training data more uniform Exactly And if if then if this data becomes noisy it b it becomes eff effectively becomes less noisy basically Hmm But of course you cannot add too much noise because then s then clean recognition goes down but I mean yet to be seen how much a very simple technique Yes indeed a very simple technique you just take your spectrum and and use whatever is coming from FFT add constant Hmm you know on onto power spectrum That that Or the other thing is of course if you have a spectrum what you can s start doing you can leave start leaving out the p the parts which are uh uh low in energy and then perhaps uh one could try to find a a model to such a spectrum Because a model will still try to to to put the the continuation basically of the of the model into these parts where the issue set to zero what we want to try I have a visitor from Brno a kind of like young faculty pretty so he so so looking into that Hmm And then most of the effort is uh now also aimed at this e e TRAP recognition This uh this is this recognition from temporal patterns Hmm What is that Ah you don't know about Hmm TRAPS The TRAPS sound familiar I but Yeah I mean don't tha This is familiar like sort of because we gave you the name but what it is is that normally what you do is that you recognize uh speech based on a shortened spectrum Essentially LPC mel cepstrum uh everything starts with a spectral slice Uh so if you s So given the spectrogram you essentially are sliding sliding the spectrogram along the uh f frequency axis and you keep shifting this thing and you have a spectrogram So you can say well you can also take the time trajectory of the energy at a given frequency and what you get is then that you get a p vector And this vector can be a a s assigned to s some phoneme Namely you can say i it I will I will say that this vector will eh will will describe the phoneme which is in the center of the vector And you can try to classify based on that Hmm And you so you classi so a very different vector very different properties we don't know much about it Hmm but the truth is you have many of those vectors per Well phoneme you right many decisions And then you can start dec thinking about how to combine these decisions Hmm Exactly what yeah what it is Hmm Because if you run this uh recognition you get you still get about twenty percent error uh twenty percent correct Hmm You know on on like for the frame by frame basis so uh uh so much better than chance How wide are the uh frequency bands another thing Well c currently we start I mean we start always with critical band spectrum For various reasons But uh the latest uh observation uh is that you you you are you can get quite a big advantage of using two critical bands at the same time Are they adjacent or are they Adjacent OK adjacent And the reasons there are some reasons for that Because there are some reasons I can I could talk about will have to tell you about things like masking experiments which uh uh uh uh yield critical bands and also experiments with release of masking which actually tell you that something is happening across critical bands across bands And Well how do you how do you uh convert this uh energy over time in a particular frequency band into a vector of numbers