PhD D: Well , For for the Italian part I mean the uh the um networks are trained with noise from Aurora TI - digits ,
PhD E: Aurora - two .
PhD D: mmm .
Professor B: And the noise is different in th
PhD D: Yeah . And perhaps the noise are quite different from the noises in the speech that Italian .
Professor B: Do we have any um test sets uh in any other language that um have the same noise as in the Aurora ?
PhD D: And PhD E: Mmm , no .
PhD D: No .
PhD A: Can I ask something real quick ? In in the upper part  in the English stuff , it looks like the very best number is sixty point nine ? and that 's in the uh  the third section in the upper part under PLP JRASTA , sort of the middle column ?
PhD D: Yeah .
PhD A: I is that a noisy condition ?
PhD D: Yeah .
PhD A: So that 's matched training ? Is that what that is ?
PhD D: It 's no , the third part , so it 's uh highly mismatched . So . Training and test noise are different .
PhD A: So why do you get your best number in Wouldn't you get your best number in the clean case ?
PhD C: Well , it 's relative to the um baseline mismatching
PhD D: Yeah .
PhD A: Ah ,
PhD D: Yeah . Yeah .
PhD A: OK so these are not OK , alright , I see .
PhD C: Yeah .
PhD A: OK . And then so , in the in the um  in the non - mismatched clean case , your best one was under MFCC ? That sixty - one point four ?
PhD D: Yeah . But it 's not a clean case . It 's a noisy case but uh training and test noises are the same .
PhD A: Oh ! So this upper third ?
PhD D: So Yeah .
PhD A: Uh that 's still noisy ?
PhD D: Yeah .
PhD A: Ah , OK .
PhD D: So it 's always noisy basically ,
PhD A: Mm - hmm .
PhD D: and , well , the PhD A: I see .
PhD D: Mmm .
Professor B: OK ? Um So uh , I think this will take some looking at , thinking about . But , what is uh what is currently running , that 's uh , i that just filling in the holes here or or ? {comment} pretty much ?
PhD D: Uh , no we don't plan to fill the holes
Professor B: OK .
PhD D: but actually there is something important , is that um we made a lot of assumption concerning the on - line normalization and we just noticed uh recently that uh the approach that we were using was not uh leading to very good results when we used the straight features to HTK . Um  Mmm . So basically d if you look at the at the left of the table , the first uh row , with eighty - six , one hundred , and forty - three and seventy - five , these are the results we obtained for Italian uh with straight mmm , PLP features using on - line normalization .
Professor B: Mm - hmm .
PhD D: Mmm . And the , mmm what 's in the table , just at the left of the PLP twelve on - line normalization column , so , the numbers seventy - nine , fifty - four and uh forty - two are the results obtained by uh Pratibha with uh his on - line normalization uh her on - line normalization approach .
PhD A: Where is that ? seventy - nine , fifty
Professor B: Uh , it 's just sort of sitting right on the uh the column line .
PhD D: So .
PhD E: Fifty - one ? This PhD A: Oh I see , OK .
Professor B: Uh . Yeah .
PhD D: Just uh Yeah . So these are the results of OGI with on - line normalization and straight features to HTK . And the previous result , eighty - six and so on , are with our features straight to HTK .
Professor B: Yes . Yes .
PhD D: So what we see that is there is that um uh the way we were doing this was not correct , but still the networks are very good . When we use the networks our number are better that uh Pratibha results .
PhD E: We improve .
Professor B: So , do you know what was wrong with the on - line normalization , or ?
PhD D: Yeah . There were diff there were different things and basically , the first thing is the mmm , alpha uh value . So , the recursion uh part . um , I used point five percent , which was the default value in the  in the programs here . And Pratibha used five percent .
Professor B: Uh
PhD D: So it adapts more quickly
Professor B: Yes . Yeah .
PhD D: Um , but , yeah . I assume that this was not important because uh previous results from from Dan and show that basically the both both values g give the same same uh results . It was true on uh TI - digits but it 's not true on Italian .
Professor B: Mm - hmm .
PhD D: Uh , second thing is the initialization of the stuff . Actually , uh what we were doing is to start the recursion from the beginning of the utterance . And using initial values that are the global mean and variances measured across the whole database .
Professor B: Right . Right .
PhD D: And Pratibha did something different is that he uh she initialed the um values of the mean and variance by computing this on the twenty - five first frames of each utterance . Mmm . There were other minor differences , the fact that she used fifteen dissities instead s instead of thirteen , and that she used C - zero instead of log energy . Uh , but the main differences concerns the recursion . So . Uh , I changed the code uh and now we have a baseline that 's similar to the OGI baseline .
Professor B: OK .
PhD D: We It it 's slightly uh different because I don't exactly initialize the same way she does . Actually I start , mmm , I don't wait to a fifteen twenty - five twenty - five frames before computing a mean and the variance to e to to start the recursion .
PhD C: Mm - hmm .
Professor B: Yeah .
PhD D: I I use the on - line scheme and only start the re recursion after the twenty - five  twenty - fifth frame . But , well it 's similar . So uh I retrained the networks with these well , the the the networks are retaining with these new features .
Professor B: Mm - hmm .
PhD D: And , yeah .
Professor B: OK .
PhD D: So basically what I expect is that these numbers will a little bit go down but perhaps not not so much
Professor B: Right .
PhD D: because I think the neural networks learn perhaps to Professor B: Right .
PhD D: even if the features are not normalized . It it will learn how to normalize and Professor B: OK , but I think that given the pressure of time we probably want to draw because of that especially , we wanna draw some conclusions from this , do some reductions in what we 're looking at ,
PhD D: Yeah .
Professor B: and make some strong decisions for what we 're gonna do testing on before next week . So do you are you w did you have something going on , on the side , with uh multi - band or on on this ,
PhD D: Yeah I
Professor B: or ?
PhD D: No , I we plan to start this uh so , act actually we have discussed uh @ @ um , these what we could do more as a as a research and  and we were thinking perhaps that uh the way we use the tandem is not Uh , well , there is basically perhaps a flaw in the in the the stuff because we trained the networks If we trained the networks on the on a language and a t or a specific task ,
Professor B: Mm - hmm .
PhD D: um , what we ask is to the network is to put the bound the decision boundaries somewhere in the space .
Professor B: Mmm .
PhD D: And uh mmm and ask the network to put one , at one side of the for for a particular phoneme at one side of the boundary decision boundary and one for another phoneme at the other side . And so there is kind of reduction of the information there that 's not correct because if we change task and if the phonemes are not in the same context in the new task , obviously the decision boundaries are not  should not be at the same place .
Professor B: I di
PhD D: But the way the feature gives The the way the network gives the features is that it reduce completely the  it removes completely the information  a lot of information from the the features by uh uh placing the decision boundaries at optimal places for one kind of data but this is not the case for another kind of data .
Professor B: It 's a trade - off ,
PhD D: So Professor B: right ? Any - anyway go ahead .
PhD D: Yeah . So uh what we were thinking about is perhaps um one way to solve this problem is increase the number of outputs of the neural networks . Doing something like , um um phonemes within context and , well , basically context dependent phonemes .
Professor B: Maybe . I mean , I I think you could make the same argument , it 'd be just as legitimate , for hybrid systems as well . Right .
PhD D: Yeah but , we know that Professor B: And in fact , th things get better with context dependent versions . Right ?
PhD D: Ye - yeah but here it 's something different . We want to have features
Professor B: Yeah .
PhD D: uh well , um .
Professor B: Yeah , but it 's still true that what you 're doing is you 're ignoring you 're you 're coming up with something to represent , whether it 's a distribution , probability distribution or features , you 're coming up with a set of variables that are representing uh , things that vary w over context .
PhD D: Mm - hmm .
Professor B: Uh , and you 're putting it all together , ignoring the differences in context . That that 's true for the hybrid system , it 's true for a tandem system . So , for that reason , when you in in in a hybrid system , when you incorporate context one way or another , you do get better scores .
PhD D: Yeah .
Professor B: OK ? But I it 's it 's a big deal to get that . I I 'm I 'm sort of And once you the other thing is that once you represent start representing more and more context it is uh much more um specific to a particular task in language . So um Uh , the  the acoustics associated with uh a particular context , for instance you may have some kinds of contexts that will never occur in one language and will occur frequently in the other , so the qu the issue of getting enough training for a particular kind of context becomes harder . We already actually don't have a huge amount of training data um
PhD D: Yeah , but mmm , I mean , the the way we we do it now is that we have a neural network and basically the net network is trained almost to give binary decisions .
Professor B: Right .
PhD D: And uh binary decisions about phonemes . Nnn Uh It 's Professor B: Almost . But I mean it it it does give a distribution .
PhD D: Yeah .
Professor B: It 's and and it is true that if there 's two phones that are very similar , that uh the  i it may prefer one but it will give a reasonably high value to the other , too .
PhD D: Yeah . Yeah , sure but uh So basically it 's almost binary decisions and um the idea of using more classes is to get something that 's less binary decisions .
Professor B: Oh no , but it would still be even more of a binary decision . It it 'd be even more of one . Because then you would say that in that this phone in this context is a one , but the same phone in a slightly different context is a zero .
PhD D: But yeah , but Professor B: That would be even even more distinct of a binary decision . I actually would have thought you 'd wanna go the other way and have fewer classes .
PhD D: Yeah , but if Professor B: Uh , I mean for instance , the the thing I was arguing for before , but again which I don't think we have time to try , is something in which you would modify the code so you could train to have several outputs on and use articulatory features
PhD D: Mmm . Mm - hmm .
Professor B: cuz then that would that would go  that would be much broader and cover many different situations . But if you go to very very fine categories , it 's very binary .
PhD D: Mmm . Yeah , but I think Yeah , perhaps you 're right , but you have more classes so you you have more information in your features . So , Um You have more information in the uh
Professor B: Mm - hmm . True .
PhD D: posteriors vector um which means that But still the information is relevant
Professor B: Mm - hmm .
PhD D: because it 's it 's information that helps to discriminate ,
Professor B: Mm - hmm .
PhD D: if it 's possible to be able to discriminate among the phonemes in context .
Professor B: Well it 's it 's  it 's an interesting thought .
PhD D: But the Professor B: I mean we we could disagree about it at length
PhD D: Mmm .
Professor B: but the the real thing is if you 're interested in it you 'll probably try it
PhD D: Mmm .
Professor B: and  and we 'll see . But but what I 'm more concerned with now , as an operational level , is uh , you know ,
PhD D: Mmm .
Professor B: what do we do in four or five days ? Uh , and  so we have to be concerned with Are we gonna look at any combinations of things , you know once the nets get retrained so you have this problem out of it .
PhD D: Mmm .
Professor B: Um , are we going to look at multi - band ? Are we gonna look at combinations of things ? Uh , what questions are we gonna ask , uh now that , I mean , we should probably turn shortly to this O G I note . Um , how are we going to combine with what they 've been focusing on ? Uh , Uh we haven't been doing any of the L D A RASTA sort of thing .
PhD D: Mm - hmm .
Professor B: And they , although they don't talk about it in this note , um , there 's um , the issue of the um Mu law business uh versus the logarithm , um , so .
PhD D: Mm - hmm .
Professor B: So what i what is going on right now ? What 's right you 've got nets retraining , Are there is there are there any H T K trainings testings going on ?
PhD D: N
PhD E: I I I 'm trying the HTK with eh , PLP twelve on - line delta - delta and MSG filter together .
Professor B: The combination , I see .
PhD E: The combination , yeah . But I haven't result at this moment .
Professor B: MSG and and PLP .
PhD E: Yeah .
Professor B: And is this with the revised on - line normalization ?
PhD E: Ye - Uh , with the old older ,
PhD D: Yeah .
Professor B: Old one . So it 's using all the nets for that
PhD E: yeah .
Professor B: but again we have the hope that it  We have the hope that it  maybe it 's not making too much difference ,
PhD E: Yeah . But We can know soon .
Professor B: but but
PhD E: Maybe .
Professor B: yeah .
PhD E: I don't know .
PhD D: Yeah .
Professor B: Uh , OK .
PhD D: Uh so there is this combination , yeah . Working on combination obviously .
PhD E: Mm - hmm .
PhD D: Um , I will start work on multi - band . And we plan to work also on the idea of using both features and net outputs .
PhD E :