Professor B: Yes , OK . OK . So uh I also copied uh the results that we all got in the mail I think from uh  from OGI and we 'll go go through them also . So where are we on  on uh  our runs ?
PhD D: Uh so . uh We So As I was already said , we we mainly focused on uh four kind of features .
Professor B: Excuse me .
PhD D: The PLP , the PLP with JRASTA , the MSG , and the MFCC from the baseline Aurora .
Professor B: Mm - hmm .
PhD D: Uh , and we focused for the the test part on the English and the Italian . Um . We 've trained uh several neural networks on so on the TI - digits English and on the Italian data and also on the broad uh English uh French and uh Spanish databases . Mmm , so there 's our result tables here , for the tandem approach , and um , actually what we we @ @ observed is that if the network is trained on the task data it works pretty well .
Professor B: OK . Our our uh  There 's a  We 're pausing for a photo PhD C: Chicken on the grill . Try that corner .
PhD A: How about over th from the front of the room ?
PhD C: Yeah , it 's longer .
Professor B: We 're pausing for a photo opportunity here . Uh . Uh . So .
Grad F: Oh wait wait wait wait wait . Wait .
PhD C: Get out of the Yeah .
Grad F: Hold on . Hold on .
Professor B: OK .
Grad F: Let me give you a black screen .
Professor B: He 's facing this way . What ? OK , this this would be a good section for our silence detection .
Grad F: OK .
PhD C: Mm - hmm .
Professor B: Um Oh .
Grad F: Musical chairs everybody !
Professor B: OK . So um , you were saying about the training data Yeah .
PhD D: Yeah , so if the network is trained on the task data um tandem works pretty well . And uh actually we have uh , results are similar Only on ,
PhD A: Do you mean if it 's trained only on On data from just that task ,
PhD D: yeah .
PhD A: that language ?
PhD D: Just that task . But actually we didn't train network on uh both types of data I mean uh phonetically ba phonetically balanced uh data and task data .
PhD A: Mmm .
PhD D: We only did either task task data or uh broad data .
PhD A: Mm - hmm .
PhD D: Um Yeah . So ,
Professor B: So how I mean clearly it 's gonna be good then
PhD A: So what 's th
Professor B: but the question is how much worse is it if you have broad data ? I mean , my assump From what I saw from the earlier results , uh I guess last week , was that um , if you trained on one language and tested on another , say , that the results were were relatively poor .
PhD D: Mmm . Yeah .
Professor B: But but the question is if you train on one language but you have a broad coverage and then test in another , does that  is that improve things i c in comparison ?
PhD D: If we use the same language ?
Professor B: No , no , no . Different lang So um If you train on TI - digits and test on Italian digits , you do poorly , let 's say .
PhD D: Mm - hmm .
Professor B: I don't have the numbers in front of me ,
PhD D: But Yeah but I did not uh do that .
Professor B: so I 'm just imagining . E So , you didn't train on TIMIT and test on  on Italian digits , say ?
PhD D: We No , we did four four kind of of testing , actually . The first testing is with task data So , with nets trained on task data . So for Italian on the Italian speech @ @ . The second test is trained on a single language um with broad database , but the same language as the t task data .
Professor B: OK .
PhD D: But for Italian we choose Spanish which we assume is close to Italian . The third test is by using , um the three language database
Professor B: W which in PhD D: and the fourth is
Professor B: It has three languages . That 's including the w the  the PhD D: This includes Professor B: the one that it 's PhD D: Yeah .
PhD A: In
PhD D: But not digits . I mean it 's PhD A: The three languages is not digits ,
Professor B: Right .
PhD A: it 's the broad data . OK .
PhD D: Yeah And the fourth test is uh excluding from these three languages the language that is the task language .
Professor B: Oh , OK , yeah , so , that is what I wanted to know .
PhD D: Yeah .
Professor B: I just wasn't saying it very well , I guess .
PhD D: Uh , yeah . So um for uh TI - digits for ins example uh when we go from TI - digits training to TIMIT training uh we lose uh around ten percent , uh . The error rate increase u of of of ten percent , relative .
Professor B: Relative . Right .
PhD D: So this is not so bad . And then when we jump to the multilingual data it 's uh it become worse and , well Around uh , let 's say , twenty perc twenty percent further .
Professor B: Ab - about how much ?
PhD D: So . Yeah .
Professor B: Twenty percent further ?
PhD D: Twenty to to thirty percent further . Yeah .
PhD A: And so , remind me , the multilingual stuff is just the broad data . Right ? It 's not the digits .
PhD D: Yeah .
PhD A: So it 's the combination of two things there . It 's removing the task specific training and it 's adding other languages .
PhD D: Yeah . Yeah .
PhD A: OK .
PhD D: But the first step is al already removing the task s specific from from PhD A: Already , right right right .
PhD D: So .
PhD A: So they were sort of building here ?
PhD D: And we lose PhD A: OK ?
PhD D: Yeah . Uh So , basically when it 's trained on the the multilingual broad data um or number so , the the ratio of our error rates uh with the baseline error rate is around uh one point one .
Professor B: Yes . And it 's something like one point three of of the uh PhD D: So .
Professor B: I i if you compare everything to the first case at the baseline , you get something like one point one for the for the using the same language but a different task , and something like one point three for three three languages broad stuff .
PhD D: No no no . Uh same language we are at uh for at English at O point eight . So it improves , compared to the baseline . But So . Le - let me .
Professor B: I I I 'm sorry .
PhD D: Tas - task data
Professor B: I I I meant something different by baseline
PhD D: we are u Yeah .
Professor B: So let me let me Um , so , um PhD D: Mmm .
Professor B: OK , fine . Let 's let 's use the conventional meaning of baseline .
PhD D: Hmm .
Professor B: I I By baseline here I meant uh using the task specific data .
PhD D: Oh yeah , the f Yeah , OK .
Professor B: But uh  uh , because that 's what you were just doing with this ten percent .
PhD D: Yeah .
Professor B: So I was just I just trying to understand that .
PhD D: Yeah . Sure .
Professor B: So if we call a factor of w just one , just normalized to one , the word error rate that you have for using TI - digits as as training and TI - digits as test ,
PhD D: Mmm .
Professor B: uh different words , I 'm sure ,
PhD D: Mm - hmm .
Professor B: but  but uh , uh the same task and so on .
PhD D: Mm - hmm .
Professor B: If we call that " one " , then what you 're saying is that the word error rate for the same language but using uh different training data than you 're testing on , say TIMIT and so forth , it 's one point one .
PhD D: Mm - hmm . Yeah , it 's around one point one .
Professor B: Right . And if it 's PhD D: Yeah .
Professor B: you do go to three languages including the English , it 's something like one point three . That 's what you were just saying , I think .
PhD D: Ye Uh , more actually .
PhD A: One point four ?
PhD D: If I Yeah .
PhD A: So , it 's an additional thirty percent .
PhD D: What would you say ? Around one point four
Professor B: OK .
PhD D: yeah .
Professor B: And if you exclude English , from this combination , what 's that ?
PhD D: If we exclude English , um there is not much difference with the data with English .
Professor B: Aha !
PhD D: So . Yeah .
Professor B: That 's interesting . That 's interesting . Do you see ? Because Uh ,
PhD D: Uh .
Professor B: so No , that that 's important . So what what it 's saying here is just that " yes , there is a reduction in performance , when you don't um have the s when you don't have um
PhD A: Task data .
Professor B: Wait a minute , th th the PhD D: Hmm .
Professor B: No , actually it 's interesting . So it 's So when you go to a different task , there 's actually not so different . It 's when you went to these So what 's the difference between two and three ? Between the one point one case and the one point four case ? I 'm confused .
PhD A: It 's multilingual .
PhD D: Yeah . The only difference it 's is that it 's multilingual Um
Professor B: Cuz in both in both both of those cases , you don't have the same task .
PhD D: Yeah . Yeah sure .
Professor B: So is is the training data for the for this one point four case does it include the training data for the one point one case ?
PhD D: Uh yeah .
Grad F: Yeah , a fraction of it .
PhD D: A part of it , yeah .
Professor B: How m how much bigger is it ?
PhD D: Um It 's two times ,
Grad F: Yeah , um .
PhD D: actually ? Yeah . Um . The English data  No , the multilingual databases are two times the broad English data . We just wanted to keep this , w well , not too huge . So .
Professor B: So it 's two times , but it includes the but it includes the broad English data .
PhD D: I think so . Do you Uh , Yeah .
Professor B: And the broad English data is what you got this one point one with . So that 's TIMIT basically right ?
PhD D: Yeah .
Grad F: Mm - hmm .
Professor B: So it 's band - limited TIMIT . This is all eight kilohertz sampling .
PhD D: Mm - hmm .
Grad F: Mm - hmm .
PhD D: Yeah .
Grad F: Downs Right .
Professor B: So you have band - limited TIMIT , gave you uh almost as good as a result as using TI - digits on a TI - digits test . OK ?
PhD D: Hmm ?
Professor B: Um and um But , when you add in more training data but keep the neural net the same size , it um performs worse on the TI - digits . OK , now all of this is  This is noisy TI - digits , I assume ? Both training and test ?
PhD D :
Professor B: Yeah . OK . Um OK . Well . We we we may just need to uh So I mean it 's interesting that h going to a different different task didn't seem to hurt us that much , and going to a different language um It doesn't seem to matter The difference between three and four is not particularly great , so that means that whether you have the language in or not is not such a big deal .
PhD D: Mmm .
Professor B: It sounds like um uh we may need to have more of uh things that are similar to a target language or I mean . You have the same number of parameters in the neural net , you haven't increased the size of the neural net , and maybe there 's just  just not enough complexity to it to represent the variab increased variability in the in the training set . That that could be . Um So , what about So these are results with uh th that you 're describing now , that they are pretty similar for the different features or  or uh PhD D: Uh , let me check . Uh .
Professor B: Yeah .
PhD D: So . This was for the PLP ,
Professor B: Yeah .
PhD D: Um . The Yeah . For the PLP with JRASTA the  the we This is quite the same tendency , with a slight increase of the error rate , uh if we go to to TIMIT . And then it 's it gets worse with the multilingual . Um . Yeah . There there is a difference actually with b between PLP and JRASTA is that JRASTA seems to perform better with the highly mismatched condition but slightly slightly worse for the well matched condition . Mmm .
Professor B: I have a suggestion , actually , even though it 'll delay us slightly , would would you mind running into the other room and making copies of this ? Cuz we 're all sort of If we c if we could look at it , while we 're talking , I think it 'd be
PhD D: Yeah , yeah . OK .
Professor B: uh  Uh , I 'll I 'll sing a song or dance or something while you do it , too .
PhD A: So um