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