PhD C: Yeah , actually to s eh , what I observed in the HM case is that the number of deletion dramatically increases . It it doubles .
Professor B: Number of deletions .
PhD C: When I added the num the neural network it doubles the number of deletions . Yeah , so I don't you know how to interpret that , but , mmm Professor B: Yeah . Me either .
PhD C: t
PhD A: And and did an other numbers stay the same ? Insertion substitutions stay the same ?
PhD C: They p stayed the same ,
PhD A: Roughly ?
PhD C: they maybe they are a little bit uh , lower .
PhD A: Uh - huh .
PhD C: They are a little bit better . Yeah . But Professor B: Did they increase the number of deletions even for the cases that got better ?
PhD C: Mm - hmm .
Professor B: Say , for the I mean , it PhD C: No , it doesn't .
Professor B: So it 's only the highly mismatched ?
PhD C: No .
Professor B: And it Remind me again , the " highly mismatched " means that the PhD C: Clean training and Professor B: Uh , sorry ?
PhD C: It 's clean training Well , close microphone training and distant microphone , um , high speed , I think .
Professor B: Close mike training PhD C: Well The most noisy cases are the distant microphone for testing .
Professor B: Right . So Well , maybe the noise subtraction is subtracting off speech .
PhD C: Separating . Yeah .
Professor B: Wh
PhD C: But Yeah . I mean , but without the neural network it 's well , it 's better . It 's just when we add the neural networks .
Professor B: Yeah , right .
PhD C: The feature are the same except that Professor B: Uh , that 's right , that 's right . Um PhD A: Well that that says that , you know , the , um the models in in , uh , the recognizer are really paying attention to the neural net features .
PhD C: Yeah .
PhD A: Uh .
PhD C: Mm - hmm .
Professor B: But , yeah , actually {nonvocalsound} the TIMIT noises are sort of a range of noises and they 're not so much the stationary driving kind of noises , right ? It 's it 's pretty different . Isn't it ?
PhD C: Uh , there is a car noise . So there are f just four noises . Um , uh , " Car " , I think , " Babble " ,
PhD D: " Babble . "
PhD C: " Subway " , right ? and PhD D: " Street " or " Airport " or something .
PhD C: and " Street " isn't PhD D: Or " Train station " .
PhD C: " Train station " , yeah .
PhD D: Yeah .
PhD C: So it 's mostly Well , " Car " is stationary ,
Professor B: Mm - hmm .
PhD C: " Babble " , it 's a stationary background plus some voices ,
Professor B: Mm - hmm .
PhD C: some speech over it . And the other two are rather stationary also .
Professor B: Well , I I think that if you run it Actually , you maybe you remember this . When you in in the old experiments when you ran with the neural net only , and didn't have this side path , um , uh , with the the pure features as well , did it make things better to have the neural net ?
PhD C: Mm - hmm .
Professor B: Was it about the same ? Uh , w i
PhD C: It was b a little bit worse .
Professor B: Than ?
PhD C: Than just the features , yeah .
Professor B: So , until you put the second path in with the pure features , the neural net wasn't helping at all .
PhD C: Mm - hmm .
Professor B: Well , that 's interesting .
PhD C: It was helping , uh , if the features are b were bad ,
Professor B: Yeah .
PhD C: I mean . Just plain P L Ps or M F
Professor B: Yeah .
PhD C: C Cs . as soon as we added LDA on - line normalization , and all these things , then Professor B: They were doing similar enough things . Well , I still think it would be k sort of interesting to see what would happen if you just had the neural net without the side thing .
PhD C: Yeah ,
Professor B: And and the thing I I have in mind is , uh , maybe you 'll see that the results are not just a little bit worse .
PhD C: mm - hmm .
Professor B: Maybe that they 're a lot worse . You know ? And , um But if on the ha other hand , uh , it 's , say , somewhere in between what you 're seeing now and and and , uh , what you 'd have with just the pure features , then maybe there is some problem of a of a , uh , combination of these things , or correlation between them somehow .
PhD C: Mm - hmm .
Professor B: If it really is that the net is hurting you at the moment , then I think the issue is to focus on on , uh , improving the the net .
PhD C: Yeah ,
Professor B: Um .
PhD C: mm - hmm .
Professor B: So what 's the overall effe I mean , you haven't done all the experiments but you said it was i somewhat better , say , five percent better , for the first two conditions , and fifteen percent worse for the other one ? But it 's but of course that one 's weighted lower ,
PhD C: Y yeah , oh . Yeah .
Professor B: so I wonder what the net effect is .
PhD C: I d I I think it 's it was one or two percent . That 's not that bad , but it was l like two percent relative worse on SpeechDat - Car . I have to to check that . Well , I have I will .
PhD D: Well , it will overall it will be still better even if it is fifteen percent worse , because the fifteen percent worse is given like f w twenty - five point two five eight .
Professor B: Right .
PhD C: Mm - hmm . Hmm .
Professor B: Right . So the so the worst it could be , if the others were exactly the same , is four ,
PhD D: Is it like Professor B: and and , uh , in fact since the others are somewhat better PhD D: Yeah , so it 's four . Is i So either it 'll get cancelled out , or you 'll get , like , almost the same .
Professor B: Uh .
PhD C: Yeah , it was it was slightly worse .
PhD D: Slightly bad . Yeah .
PhD C: Um ,
Professor B: Yeah , it should be pretty close to cancelled out .
PhD D: Yeah .
PhD A: You know , I 've been wondering about something .
PhD C: Mm - hmm .
PhD A: In the , um a lot of the , um the Hub - five systems , um , recently have been using LDA . and and they , um They run LDA on the features right before they train the models . So there 's the the LDA is is right there before the H M
PhD D: Yeah .
PhD A: So , you guys are using LDA but it seems like it 's pretty far back in the process .
PhD D: Uh , this LDA is different from the LDA that you are talking about . The LDA that you saying is , like , you take a block of features , like nine frames or something , {comment} and then do an LDA on it ,
PhD A: Yeah . Uh - huh .
PhD D: and then reduce the dimensionality to something like twenty - four or something like that .
PhD A: Yeah , you c you c you can .
PhD D: And then feed it to HMM .
PhD A: I mean , it 's you know , you 're just basically i
PhD D: Yeah , so this is like a two d two dimensional tile .
PhD A: You 're shifting the feature space . Yeah .
PhD D: So this is a two dimensional tile . And the LDA that we are f applying is only in time , not in frequency high cost frequency . So it 's like more like a filtering in time , rather than doing a r
PhD A: Ah . OK . So what i what about , um i u what i w I mean , I don't know if this is a good idea or not , but what if you put ran the other kind of LDA , uh , on your features right before they go into the HMM ?
PhD D: Uh , it PhD C: Mm - hmm . No , actually , I think i
PhD D: m
PhD C: Well . What do we do with the ANN is is something like that except that it 's not linear . But it 's it 's like a nonlinear discriminant analysis .
PhD A: Yeah . Right , it 's the It 's Right . The So Yeah , so it 's sort of like PhD C: But .
PhD A: The tandem stuff is kind of like i nonlinear LDA .
PhD C: Yeah . It 's PhD A: I g
PhD C: Yeah .
PhD A: Yeah .
Professor B: Yeah .
PhD A: But I mean , w but the other features that you have , um , th the non - tandem ones ,
PhD C: Uh . Mm - hmm . Yeah , I know . That that Yeah . Well , in the proposal , they were transformed u using PCA , but PhD A: Uh - huh .
PhD C: Yeah , it might be that LDA could be better .
Professor B: The a the argument i is kind of i in and it 's not like we really know , but the argument anyway is that , um , uh , we always have the prob I mean , discriminative things are good . LDA , neural nets , they 're good .
PhD A: Yeah .
Professor B: Uh , they 're good because you you you learn to distinguish between these categories that you want to be good at distinguishing between . And PCA doesn't do that . It PAC - PCA low - order PCA throws away pieces that are uh , maybe not not gonna be helpful just because they 're small , basically .
PhD A: Right .
Professor B: But , uh , the problem is , training sets aren't perfect and testing sets are different . So you f you you face the potential problem with discriminative stuff , be it LDA or neural nets , that you are training to discriminate between categories in one space but what you 're really gonna be g getting is is something else .
PhD A: Uh - huh .
Professor B: And so , uh , Stephane 's idea was , uh , let 's feed , uh , both this discriminatively trained thing and something that 's not . So you have a good set of features that everybody 's worked really hard to make ,
PhD A: Yeah .
Professor B: and then , uh , you you discriminately train it , but you also take the path that that doesn't have that ,
PhD A: Uh - huh .
Professor B: and putting those in together . And that that seem So it 's kind of like a combination of the uh , what , uh , Dan has been calling , you know , a feature uh , you know , a feature combination versus posterior combination or something . It 's it 's , you know , you have the posterior combination but then you get the features from that and use them as a feature combination with these these other things . And that seemed , at least in the last one , as he was just saying , he he when he only did discriminative stuff , i it actually was was it didn't help at all in this particular case .
PhD A: Yeah .
Professor B: There was enough of a difference , I guess , between the testing and training . But by having them both there The fact is some of the time , the discriminative stuff is gonna help you .
PhD A: Mm - hmm .
Professor B: And some of the time it 's going to hurt you ,
PhD A: Right .
Professor B: and by combining two information sources if , you know if if PhD A: So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that Professor B: That i i I think that 's counter to that idea .
PhD A: Yeah , right .
Professor B: Now , again , it 's we 're just trying these different things . We don't really know what 's gonna work best . But if that 's the hypothesis , at least it would be counter to that hypothesis to do that .
PhD A: Right .
Professor B: Um , and in principle you would think that the neural net would do better at the discriminant part than LDA .
PhD A: Right . Yeah . Well y
Professor B: Though , maybe not .
PhD A: Yeah . Exactly . I mean , we , uh we were getting ready to do the tandem , uh , stuff for the Hub - five system , and , um , Andreas and I talked about it , and the idea w the thought was , " Well , uh , yeah , that i you know th the neural net should be better , but we should at least have uh , a number , you know , to show that we did try the LDA in place of the neural net , so that we can you know , show a clear path .
Professor B: Right .
PhD A: You know , that you have it without it , then you have the LDA , then you have the neural net , and you can see , theoretically . So . I was just wondering I I Professor B: Well , I think that 's a good idea .
PhD A: Yeah .
Professor B: Did did you do that
PhD A: Um . No .
Professor B: or tha that 's a PhD A: That 's what that 's what we 're gonna do next as soon as I finish this other thing . So .
Professor B: Yeah . Yeah . No , well , that 's a good idea . I I PhD A: We just want to show .
Professor B: i Yeah .
PhD A: I mean , it everybody believes it ,
Professor B: Oh , no it 's a g
PhD A: but you know , we just Professor B: No , no , but it might not not even be true .
PhD A: Yeah .
Professor B: I mean , it 's it 's it 's it 's it 's a great idea . I mean , one of the things that always disturbed me , uh , in the the resurgence of neural nets that happened in the eighties was that , um , a lot of people Because neural nets were pretty easy to to use a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh uh , versions of them .
PhD A: Yeah . Mm - hmm . Yeah .
Professor B: And , uh , people were doing recurrent nets but not looking at IIR filters , and You know , I mean , uh , so I think , yeah , it 's definitely a good idea to try it .
PhD A: Yeah , and everybody 's putting that on their systems now , and so , I that 's what made me wonder about this ,
Professor B: Well , they 've been putting them in their systems off and on for ten years ,
PhD A: but .
Professor B: but but but , uh ,
PhD A: Yeah , what I mean is it 's it 's like in the Hub - five evaluations , you know , and you read the system descriptions and everybody 's got , you know , LDA on their features .
Professor B: And now they all have that . I see .
PhD A: And so .
Professor B: Yeah .
PhD A: Uh .
PhD C: It 's the transformation they 're estimating on Well , they are trained on the same data as the final HMM are .
PhD A: Yeah , so it 's different . Yeah , exactly . Cuz they don't have these , you know , mismatches that that you guys have .
PhD C: Mm - hmm .
PhD A: So that 's why I was wondering if maybe it 's not even a good idea .
PhD C: Mm - hmm .
PhD A: I don't know . I I don't know enough about it ,
PhD C: Mm - hmm .
PhD A: but Um .
Professor B: I mean , part of why I I think part of why you were getting into the KLT Y you were describing to me at one point that you wanted to see if , uh , you know , getting good orthogonal features was and combining the the different temporal ranges was the key thing that was happening or whether it was this discriminant thing , right ? So you were just trying I think you r I mean , this is it doesn't have the LDA aspect but th as far as the orthogonalizing transformation , you were trying that at one point , right ?
PhD C: Mm - hmm .
Professor B: I think you were .
PhD C: Mm - hmm . Yeah .
Professor B: Does something . It doesn't work as well . Yeah . Yeah .