PhD A: Go ahead . Ah , while you 're gone I 'll ask s some of my questions .
Professor B: Yeah .
PhD A: Um .
Professor B: Yeah . Uh , this way and just slightly to the left , yeah .
PhD A: The um What was Was this number forty or It was roughly the same as this one , he said ? When you had the two language versus the three language ?
Professor B: Um . That 's what he was saying .
PhD A: That 's where he removed English ,
Grad F: Yeah .
PhD A: right ?
Professor B: Right .
Grad F: It sometimes , actually , depends on what features you 're using .
Professor B: Yeah . But but i it sounds like Grad F: Um , but   He Mm - hmm .
Professor B: I mean . That 's interesting because it it seems like what it 's saying is not so much that you got hurt uh because you uh didn't have so much representation of English , because in the other case you don't get hurt any more , at least when it seemed like uh it it might simply be a case that you have something that is just much more diverse ,
PhD A: Mm - hmm .
Professor B: but you have the same number of parameters representing it .
PhD A: Mm - hmm . I wonder were um all three of these nets using the same output ? This multi - language uh labelling ?
Grad F: He was using uh sixty - four phonemes from SAMPA .
PhD A: OK , OK .
Grad F: Yeah .
PhD A: So this would  From this you would say , " well , it doesn't really matter if we put Finnish into the training of the neural net , if there 's gonna be , you know , Finnish in the test data . " Right ?
Professor B: Well , it 's it sounds  I mean , we have to be careful , cuz we haven't gotten a good result yet .
PhD A: Yeah .
Professor B: And comparing different bad results can be tricky .
PhD A: Hmm .
Professor B: But I I I  I think it does suggest that it 's not so much uh uh cross language as cross type of speech .
PhD A: Mm - hmm .
Professor B: It 's it 's um  But we did Oh yeah , the other thing I was asking him , though , is that I think that in the case Yeah , you you do have to be careful because of com compounded results . I think we got some earlier results in which you trained on one language and tested on another and you didn't have three , but you just had one language . So you trained on one type of digits and tested on another . Didn - Wasn't there something of that ? Where you , say , trained on Spanish and tested on on TI - digits , or the other way around ? Something like that ?
PhD E: No .
Professor B: I thought there was something like that , that he showed me last week . We 'll have to wait till we get PhD A: Yeah , that would be interesting .
Professor B: Um , This may have been what I was asking before , Stephane , but  but , um , wasn't there something that you did , where you trained on one language and tested on another ? I mean no no mixture but just Grad F: I 'll get it for you .
PhD D: Uh , no , no .
Professor B: We 've never just trained on one lang
PhD D: Training on a single language , you mean , and testing on the other one ?
Professor B: Yeah .
PhD D: Uh , no .
PhD E: Not yet .
PhD D: So the only task that 's similar to this is the training on two languages , and {comment} that Professor B: But we 've done a bunch of things where we just trained on one language . Right ? I mean , you haven't you haven't done all your tests on multiple languages .
PhD D: Uh , No . Either thi this is test with uh the same language but from the broad data , or it 's test with uh different languages also from the broad data , excluding the So , it 's it 's three or three and four .
PhD E: The early experiment that PhD A: Did you do different languages from digits ?
PhD D: Uh . No . You mean training digits on one language and using the net to recognize on the other ?
PhD A: Digits on another language ?
PhD D: No .
Professor B: See , I thought you showed me something like that last week . You had a you had a little PhD D: Uh , No , I don't think so .
Professor B: Um What PhD C: These numbers are uh ratio to baseline ?
Professor B: So , I mean wha what 's the PhD D: So .
Professor B: This this chart this table that we 're looking at is um , show is all testing for TI - digits , or ?
Grad F: Bigger is worse .
PhD D: So you have uh basically two uh parts .
Grad F: This is error rate , I think .
PhD C: Ratio .
Grad F: No . No .
PhD D: The upper part is for TI - digits
Grad F: Yeah , yeah , yeah .
PhD D: and it 's divided in three rows of four four rows each .
Grad F: Mm - hmm .
Professor B: Yeah .
PhD D: And the first four rows is well - matched , then the s the second group of four rows is mismatched , and finally highly mismatched . And then the lower part is for Italian and it 's the same  the same thing .
PhD A: So , so the upper part is training TI - digits ?
PhD D: So . It 's it 's the HTK results , I mean . So it 's HTK training testings with different kind of features
PhD A: Ah .
PhD D: and what appears in the uh left column is the networks that are used for doing this .
Professor B: Hmm .
PhD D: So . Uh Yeah .
Professor B: Well , What was is that i What was it that you had done last week when you showed Do you remember ? Wh - when you showed me the your table last week ?
PhD D: It - It was part of these results . Mmm . Mmm .
PhD A: So where is the baseline for the TI - digits located in here ?
PhD D: You mean the HTK Aurora baseline ?
PhD A: Yeah .
PhD D: It 's uh the one hundred number . It 's , well , all these numbers are the ratio with respect to the baseline .
PhD A: Ah ! Ah , OK , OK .
Professor B: So this is word word error rate , so a high number is bad .
PhD D: Yeah , this is a word error rate ratio .
PhD E: Yeah .
PhD A: OK , I see .
PhD D: Yeah . So , seventy point two means that we reduced the error rate uh by thirty thirty percent .
PhD A: OK , OK , gotcha .
PhD D: So .
Professor B: OK , so if we take
PhD D: Hmm .
Professor B: uh um let 's see PLP uh with on - line normalization and delta - del so that 's this thing you have circled here in the second column ,
PhD D: Yeah .
Professor B: um and " multi - English " refers to what ?
PhD D: To TIMIT . Mmm . Then you have uh MF , MS and ME which are for French , Spanish and English . And , yeah . Actually I  I uh forgot to say that the multilingual net are trained on uh features without the s derivatives uh but with increased frame numbers . Mmm . And we can we can see on the first line of the table that it it  it 's slightly slightly worse when we don't use delta but it 's not  not that much .
Professor B: Right . So w w So , I 'm sorry . I missed that . What 's MF , MS and ME ?
PhD A: Multi - French , Multi - Spanish
PhD D: So . Multi - French , Multi - Spanish , and Multi - English .
Professor B: Uh OK . So , it 's uh broader vocabulary . Then And PhD D: Yeah .
Professor B: OK so I think what I 'm what I saw in your smaller chart that I was thinking of was was there were some numbers I saw , I think , that included these multiple languages and it and I was seeing that it got worse . I I think that was all it was . You had some very limited results that at that point
PhD D: Yeah .
Professor B: which showed having in these these other languages . In fact it might have been just this last category , having two languages broad that were where where English was removed . So that was cross language and the and the result was quite poor . What I  we hadn't seen yet was that if you added in the English , it 's still poor .
PhD D: Yeah .
Professor B: Uh  Um now , what 's the noise condition um of the training data PhD D: Still poor .
Professor B: Well , I think this is what you were explaining . The noise condition is the same It 's the same uh Aurora noises uh , in all these cases for the training .
PhD D: Yeah . Yeah .
Professor B: So there 's not a statistical sta a strong st statistically different noise characteristic between uh the training and test
PhD D: No these are the s s s same noises ,
Professor B: and yet we 're seeing some kind of effect PhD D: yeah . At least at least for the first  for the well - matched ,
Grad F: Well matched condition .
Professor B: Right .
PhD D: yeah .
Professor B: So there 's some kind of a a an effect from having these uh this broader coverage um Now I guess what we should try doing with this is try testing these on u this same sort of thing on you probably must have this lined up to do . To try the same t with the exact same training , do testing on the other languages .
PhD D: Mmm .
Professor B: On on um So . Um , oh I well , wait a minute . You have this here , for the Italian . That 's right . OK , so , So .
PhD D: Yeah . Yeah , so for the Italian the results are uh stranger um Mmm . So what appears is that perhaps Spanish is not very close to Italian 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 .
Professor B: Mm - hmm .
PhD D: Mmm . Uh .
Professor B: Well , I mean , let 's see . Is there any difference in So it 's in the uh So you 're saying that when you train on English and uh and and test on PhD D: Yeah .