Meeting transcript:
speaker: B
text: OK .
speaker: C
text: Oh , I don't <disfmarker>
speaker: A
text: I think I 'm zero .
speaker: B
text: Wow !
speaker: E
text: Ah
speaker: F
text: Wh - what causes the crash ?
speaker: B
text: Unprecedented .
speaker: C
text: Hello , hello , hello , hello .
speaker: A
text: Did you fix something ?
speaker: C
text: Hello .
speaker: E
text: Five , five .
speaker: C
text: Hello , hello .
speaker: F
text: Oh , maybe it 's the turning <disfmarker> turning off and turning on of the mike ,
speaker: F
text: right ?
speaker: B
text: Uh , you think that 's you ?
speaker: B
text: Oh .
speaker: C
text: Aaa - aaa - aaa .
speaker: F
text: Yeah , OK , mine 's working .
speaker: C
text: OK .
speaker: C
text: That 's me .
speaker: B
text: OK .
speaker: B
text: OK .
speaker: B
text: So , um I guess we are <pause> um <pause> gonna do the digits at the end .
speaker: B
text: Uh
speaker: D
text: Channel <disfmarker> channel three ,
speaker: D
text: yeah .
speaker: E
text: Mmm , channel five ?
speaker: D
text: OK .
speaker: C
text: Channel two .
speaker: E
text: Doesn't work ?
speaker: C
text: Two .
speaker: B
text: Yeah , that 's the mike number there , uh <pause> Uh , mike number five , and <pause> channel <disfmarker> channel four .
speaker: E
text: No ? Ah ,
speaker: A
text: Is it written on her sheet , I believe .
speaker: D
text: Mike four .
speaker: F
text: Watch this .
speaker: E
text: era el cuatro .
speaker: F
text: Yep , that 's me .
speaker: E
text: Yeah .
speaker: A
text: But , channel
speaker: E
text: Yeah yeah yeah .
speaker: B
text: This is you .
speaker: E
text: OK .
speaker: E
text: I saw that .
speaker: E
text: Ah <disfmarker> yeah , it 's OK .
speaker: B
text: Yeah .
speaker: B
text: And I 'm channel uh two I think ,
speaker: C
text: Ooo .
speaker: B
text: or channel <disfmarker>
speaker: C
text: I think I 'm channel two .
speaker: B
text: Oh , I 'm channel <disfmarker> must be channel one .
speaker: E
text: Channel <disfmarker> <vocalsound> I decided to talk about that .
speaker: B
text: Channel one ?
speaker: B
text: Yes , OK .
speaker: B
text: OK .
speaker: B
text: So uh <pause> I also copied uh the results that we all got in the mail I think from uh <disfmarker> <pause> from OGI
speaker: B
text: and we 'll go <disfmarker> go through them also .
speaker: B
text: So where are we on <disfmarker> <pause> on uh <vocalsound> <pause> our runs ?
speaker: D
text: Uh so . <pause> uh <disfmarker> We <disfmarker> So <pause> As I was already said , we <disfmarker> we mainly focused on uh four kind of features .
speaker: B
text: Excuse me .
speaker: D
text: The PLP , the PLP with JRASTA , the MSG , and the MFCC from the baseline Aurora .
speaker: B
text: Mm - hmm .
speaker: D
text: Uh , and we focused for the <disfmarker> the test part on the English and the Italian .
speaker: D
text: Um . We 've trained uh several neural networks on <disfmarker>
speaker: D
text: so <disfmarker> on the TI - digits English <pause> and on the Italian data
speaker: D
text: and also on the broad uh <pause> English uh French and uh Spanish databases .
speaker: D
text: Mmm , so there 's our result tables here , for the tandem approach ,
speaker: D
text: and um , actually what we <disfmarker> we @ @ observed is that if the network is trained on the task data it works pretty well .
speaker: B
text: OK .
speaker: C
text: Chicken on the grill .
speaker: B
text: Our <disfmarker> our uh <disfmarker> <pause> There 's a <disfmarker> <pause> We 're pausing for a photo <disfmarker>
speaker: C
text: Try that corner .
speaker: A
text: How about over th from the front of the room ?
speaker: C
text: Yeah , it 's longer .
speaker: B
text: We 're pausing for a photo opportunity here .
speaker: B
text: Uh . <vocalsound> Uh .
speaker: B
text: So .
speaker: F
text: Oh wait wait wait wait wait . Wait .
speaker: C
text: Get out of the <disfmarker>
speaker: C
text: Yeah .
speaker: F
text: Hold on .
speaker: F
text: Hold on .
speaker: B
text: OK .
speaker: F
text: Let me give you a black screen .
speaker: B
text: He 's facing this way .
speaker: B
text: What ?
speaker: B
text: OK , this <disfmarker> this would be a <pause> good section for our silence detection .
speaker: F
text: OK .
speaker: C
text: Mm - hmm .
speaker: B
text: Um Oh .
speaker: F
text: Musical chairs everybody !
speaker: B
text: OK . So um , <pause> you were saying <pause> about the training data <disfmarker>
speaker: D
text: Yeah ,
speaker: B
text: Yeah .
speaker: D
text: so if the network is trained on the task data um <pause> tandem works pretty well .
speaker: D
text: And uh actually we have uh ,
speaker: D
text: results are similar Only on ,
speaker: A
text: Do you mean if it 's trained only on <disfmarker> On data from just that task ,
speaker: D
text: yeah .
speaker: A
text: that language ?
speaker: D
text: Just that task .
speaker: D
text: But actually we didn't train network on <pause> uh both types of data
speaker: D
text: I mean <pause> uh <pause> phonetically ba
speaker: D
text: phonetically balanced uh data and task data .
speaker: D
text: We only did either task <disfmarker> task data or <pause> uh broad <pause> data .
speaker: A
text: Mmm .
speaker: A
text: Mm - hmm .
speaker: D
text: Um <pause> Yeah .
speaker: D
text: So ,
speaker: B
text: So how <disfmarker>
speaker: B
text: I mean <disfmarker> clearly it 's gonna be good then
speaker: A
text: So what 's th
speaker: B
text: but the question is how much <pause> worse is it <pause> if you have broad data ?
speaker: B
text: I mean , <pause> my assump
speaker: B
text: From what I saw from the earlier results , uh I guess last week , <pause> was that um , <pause> if you <pause> trained on one language and tested on another , say , that <pause> the results were <disfmarker> were relatively poor .
speaker: D
text: Mmm . Yeah .
speaker: B
text: But <disfmarker> but the question is if you train on one language <pause> but you have a broad coverage <pause> and then test in another , <pause> does that <disfmarker> <pause> is that improve things <pause> i c in comparison ?
speaker: D
text: If we use the same language ?
speaker: B
text: No , no , no .
speaker: B
text: Different lang
speaker: B
text: So <pause> um <pause> If you train on TI - digits <pause> and test on Italian digits , <pause> you do poorly , <pause> let 's say .
speaker: D
text: Mm - hmm .
speaker: B
text: I don't have the numbers in front of me ,
speaker: D
text: But <disfmarker> Yeah but I did not uh do that .
speaker: B
text: so I 'm just imagining .
speaker: B
text: E So , you didn't train on <pause> TIMIT and test on <disfmarker> <pause> on Italian digits , say ?
speaker: D
text: We <disfmarker> No , we did four <disfmarker> four kind of <disfmarker> of testing , actually .
speaker: D
text: The first testing is <pause> with task data <disfmarker>
speaker: D
text: So , with nets trained on task data .
speaker: D
text: So for Italian on the Italian speech @ @ .
speaker: D
text: The second test is trained on a single language um with broad database ,
speaker: D
text: but the same language as the t task data .
speaker: B
text: OK .
speaker: D
text: But for Italian we choose Spanish which <pause> we assume is close to Italian .
speaker: D
text: The third test is by using , um the three language database
speaker: D
text: and the fourth is
speaker: B
text: W which in <disfmarker>
speaker: B
text: It has three languages .
speaker: B
text: That 's including the w the <disfmarker> <pause> the <disfmarker>
speaker: D
text: This includes <disfmarker>
speaker: B
text: the one that it 's <disfmarker>
speaker: D
text: Yeah .
speaker: D
text: But <pause> not digits .
speaker: A
text: In
speaker: D
text: I mean it 's <disfmarker>
speaker: B
text: Right .
speaker: A
text: The three languages <pause> is not digits ,
speaker: A
text: it 's the broad <pause> data .
speaker: D
text: Yeah
speaker: A
text: OK .
speaker: D
text: And the fourth test is uh <pause> excluding from these three languages the language <pause> that is <pause> the task language .
speaker: B
text: Oh , OK ,
speaker: B
text: yeah , so , that is what I wanted to know .
speaker: D
text: Yeah .
speaker: B
text: I just wasn't saying it very well , I guess .
speaker: D
text: Uh , yeah .
speaker: D
text: So um <pause> for uh TI - digits for ins
speaker: D
text: example <pause> uh when we go from TI - digits training to <pause> TIMIT training <pause> uh we lose <pause> uh around ten percent ,
speaker: D
text: uh . The error rate increase u of <disfmarker> of <disfmarker> of ten percent , relative .
speaker: B
text: Relative .
speaker: B
text: Right .
speaker: D
text: So this is not so bad .
speaker: D
text: And then when we jump to the multilingual data it 's uh it become worse
speaker: D
text: and , well Around uh , let 's say , <pause> twenty perc twenty percent further .
speaker: B
text: Ab - about how much ?
speaker: D
text: So .
speaker: D
text: Yeah .
speaker: B
text: Twenty percent further ?
speaker: D
text: Twenty to <disfmarker> to thirty percent further .
speaker: D
text: Yeah .
speaker: A
text: And so , remind me ,
speaker: A
text: the multilingual stuff is just the broad data .
speaker: A
text: Right ?
speaker: D
text: Yeah .
speaker: A
text: It 's not the digits .
speaker: A
text: So it 's the combination of <pause> two things there .
speaker: A
text: It 's <pause> removing the <pause> task specific <pause> training and <pause> it 's adding other languages .
speaker: D
text: Yeah . Yeah .
speaker: A
text: OK .
speaker: D
text: But the first step is al already removing the task s specific from <disfmarker> from <disfmarker>
speaker: A
text: Already ,
speaker: A
text: right right right .
speaker: D
text: So .
speaker: D
text: And we lose <disfmarker>
speaker: A
text: So they were sort of building <pause> here ?
speaker: D
text: Yeah .
speaker: A
text: OK ?
speaker: D
text: Uh <pause> So , basically when it 's trained on the <disfmarker> the multilingual broad data <pause> um or number <disfmarker>
speaker: D
text: so ,
speaker: D
text: the <disfmarker> the <pause> ratio of
speaker: D
text: our error rates uh with the <pause> baseline error rate is around <pause> uh one point one .
speaker: D
text: So .
speaker: B
text: Yes . <vocalsound> And it 's something like one point three of <disfmarker> of the <pause> uh <disfmarker>
speaker: B
text: I i if you compare everything to the first case at the baseline ,
speaker: B
text: you get something like one point one for the <disfmarker> for the using the same language but a different task ,
speaker: B
text: and something like one point three <pause> for three <disfmarker> three languages <pause> broad stuff .
speaker: D
text: No no no .
speaker: D
text: Uh same language we are at uh <disfmarker> for at English at O point eight .
speaker: D
text: So it improves , <pause> compared to the baseline .
speaker: D
text: But <disfmarker>
speaker: D
text: So .
speaker: D
text: Le - let me .
speaker: B
text: I <disfmarker> I <disfmarker> I 'm sorry .
speaker: D
text: Tas - task data
speaker: D
text: we are u
speaker: B
text: I <disfmarker> I <disfmarker> I meant something different by baseline
speaker: D
text: Yeah .
speaker: B
text: So let me <disfmarker> let me <disfmarker>
speaker: B
text: Um , <pause> so , <pause> um <disfmarker>
speaker: D
text: Mmm .
speaker: B
text: OK , fine .
speaker: B
text: Let 's <disfmarker> let 's use the conventional meaning of baseline .
speaker: D
text: Hmm .
speaker: B
text: I <disfmarker> I <disfmarker> By baseline here I meant <pause> uh using the task specific data .
speaker: D
text: Oh yeah , the f Yeah , OK .
speaker: D
text: Yeah .
speaker: B
text: But uh <disfmarker> <pause> uh , because that 's what you were just doing with this ten percent .
speaker: B
text: So I was just <disfmarker> I just trying to understand that .
speaker: D
text: Yeah .
speaker: D
text: Sure .
speaker: B
text: So if we call <pause> a factor of w just one , just normalized to one , the word error rate <pause> that you have <pause> for using TI - digits as <disfmarker> as <pause> training and TI - digits as test ,
speaker: D
text: Mmm .
speaker: D
text: Mm - hmm .
speaker: B
text: uh different words , I 'm sure ,
speaker: B
text: but <disfmarker> <pause> but uh , uh the same <pause> task and so on .
speaker: D
text: Mm - hmm .
speaker: B
text: If we call that " one " , <pause> then what you 're saying is <pause> that the word error rate <pause> for the same language but using <pause> uh different training data than you 're testing on , say TIMIT and so forth , <pause> it 's one point one .
speaker: D
text: Mm - hmm .
speaker: D
text: Yeah , it 's around one point one .
speaker: B
text: Right .
speaker: B
text: And if it 's <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: you <pause> do <pause> go to <pause> three languages including the English , <pause> it 's something like one point three .
speaker: D
text: Ye
speaker: B
text: That 's what you were just saying , I think .
speaker: D
text: Uh , more actually .
speaker: A
text: One point four ?
speaker: D
text: If I <disfmarker> Yeah .
speaker: A
text: So , it 's an additional thirty percent .
speaker: D
text: What would you say ?
speaker: D
text: Around one point four
speaker: D
text: yeah .
speaker: B
text: OK .
speaker: B
text: And if you exclude <pause> English , <pause> from this combination , what 's that ?
speaker: D
text: If we exclude English , <pause> um <pause> there is <pause> not much difference with the <pause> data
speaker: D
text: with English .
speaker: D
text: So . Yeah .
speaker: B
text: Aha !
speaker: B
text: That 's interesting . <pause> That 's interesting .
speaker: B
text: Do you see ? Because <disfmarker> Uh ,
speaker: D
text: Uh .
speaker: B
text: so <disfmarker> No , that <disfmarker> that 's important .
speaker: B
text: So what <disfmarker> what it 's saying here is just that
speaker: B
text: " yes ,
speaker: B
text: there is a reduction <pause> in performance , <pause> when you don't <pause> um <pause> have the s <pause> when you don't have <pause> um
speaker: A
text: Task data .
speaker: B
text: Wait a minute ,
speaker: B
text: th th the <disfmarker>
speaker: D
text: Hmm .
speaker: B
text: No , actually <pause> it 's interesting .
speaker: B
text: So it 's <disfmarker> So when you go to a different task , there 's actually not so <pause> different .
speaker: B
text: It 's when you went to these <disfmarker>
speaker: B
text: So what 's the difference between two and three ?
speaker: B
text: Between the one point one case and the one point four case ?
speaker: B
text: I 'm confused .
speaker: A
text: It 's multilingual .
speaker: D
text: Yeah .
speaker: D
text: The only difference it 's <disfmarker> is that it 's multilingual <disfmarker>
speaker: D
text: Um
speaker: B
text: Cuz in both <disfmarker> in both <disfmarker> both of those cases , you don't have the same task .
speaker: D
text: Yeah .
speaker: D
text: Yeah sure .
speaker: B
text: So is <disfmarker> is the training data for the <disfmarker> for this one point four case <disfmarker>
speaker: B
text: does it include the training data for the one point one case ?
speaker: D
text: Uh yeah .
speaker: F
text: Yeah , a fraction of it .
speaker: D
text: A part of it ,
speaker: D
text: yeah .
speaker: B
text: How m how much bigger is it ?
speaker: D
text: Um <pause> It 's two times ,
speaker: F
text: Yeah , um .
speaker: D
text: actually ?
speaker: D
text: Yeah .
speaker: D
text: Um . The English data <disfmarker> <pause> No , the multilingual databases are two times the <pause> broad English <pause> data .
speaker: D
text: We just wanted to keep this , w well , not too huge .
speaker: D
text: So .
speaker: B
text: So it 's two times ,
speaker: B
text: but it includes the <disfmarker> but it includes the broad English data .
speaker: D
text: I think so .
speaker: D
text: Do you <disfmarker>
speaker: D
text: Uh , Yeah .
speaker: B
text: And the broad English data is what you got this one point one <pause> with .
speaker: B
text: So that 's TIMIT basically
speaker: B
text: right ?
speaker: D
text: Yeah .
speaker: F
text: Mm - hmm .
speaker: B
text: So it 's band - limited TIMIT .
speaker: F
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: This is all eight kilohertz sampling .
speaker: D
text: Yeah .
speaker: F
text: Downs
speaker: F
text: Right .
speaker: B
text: So you have band - limited TIMIT , <pause> gave you uh almost as good as a result as using TI - digits <pause> on a TI - digits test .
speaker: B
text: OK ?
speaker: D
text: Hmm ?
speaker: B
text: Um <pause> and <pause> um
speaker: B
text: But , <pause> when you add in more training data but keep the neural net the same size , <pause> it <pause> um performs worse on the TI - digits .
speaker: B
text: OK , now all of this is <disfmarker> <pause> This is noisy <pause> TI - digits , I assume ?
speaker: B
text: Both training and test ?
speaker: B
text: Yeah .
speaker: B
text: OK .
speaker: B
text: Um
speaker: B
text: OK . Well . <pause> We <disfmarker> we <disfmarker> we may just need to uh <disfmarker>
speaker: B
text: So I mean it 's interesting that h going to a different <disfmarker> different task didn't seem to hurt us that much ,
speaker: B
text: and going to a different language um
speaker: B
text: It doesn't seem to matter <disfmarker>
speaker: B
text: The difference between three and four is not particularly great ,
speaker: B
text: so that means that <pause> whether you have the language in or not is not such a big deal .
speaker: D
text: Mmm .
speaker: B
text: It sounds like um <pause> uh <pause> we may need to have more <pause> of uh things that are similar to a target language
speaker: B
text: or <disfmarker> I mean . <pause> You have the same number of parameters in the neural net ,
speaker: B
text: you haven't increased the size of the neural net ,
speaker: B
text: and maybe there 's just <disfmarker> <pause> just not enough <pause> complexity to it to represent <pause> the variab increased variability in the <disfmarker> in the training set .
speaker: B
text: That <disfmarker> that could be .
speaker: B
text: Um <pause> So , what about <disfmarker>
speaker: B
text: So these are results with <pause> uh th <pause> that you 're describing now , that <pause> they are pretty similar for the different features or <disfmarker> <pause> or uh <disfmarker>
speaker: D
text: Uh , let me check .
speaker: D
text: Uh .
speaker: B
text: Yeah .
speaker: D
text: So . This was for the PLP ,
speaker: B
text: Yeah .
speaker: D
text: Um . The <disfmarker>
speaker: D
text: Yeah .
speaker: D
text: For the PLP with JRASTA the <disfmarker> <pause> the <disfmarker> we <disfmarker>
speaker: D
text: This is quite the same <pause> tendency , <pause> with a slight increase of the error rate , <pause> uh if we go to <disfmarker> to TIMIT .
speaker: D
text: And then it 's <disfmarker> it gets worse with the multilingual .
speaker: D
text: Um . Yeah .
speaker: D
text: There <disfmarker> there is a difference actually with <disfmarker> b between PLP and JRASTA
speaker: D
text: is that <pause> JRASTA <pause> seems to <pause> perform better with the highly mismatched <pause> condition <pause> but slightly <disfmarker> slightly worse <pause> for the well matched condition .
speaker: D
text: Mmm .
speaker: B
text: I have a suggestion , actually ,
speaker: B
text: even though it 'll delay us slightly ,
speaker: B
text: would <disfmarker> would you mind <pause> running into the other room and making <pause> copies of this ?
speaker: B
text: Cuz we 're all sort of <disfmarker>
speaker: D
text: Yeah , yeah .
speaker: B
text: If we c if we could look at it , while we 're talking , I think it 'd be
speaker: D
text: OK .
speaker: B
text: uh <disfmarker> <pause> Uh , I 'll <disfmarker> I 'll sing a song or dance or something while you <vocalsound> do it , too .
speaker: F
text: Alright .
speaker: A
text: So um <disfmarker>
speaker: A
text: Go ahead .
speaker: A
text: Ah , while you 're gone I 'll ask s some of my questions .
speaker: B
text: Yeah .
speaker: A
text: Um .
speaker: B
text: Yeah .
speaker: B
text: Uh , this way and just slightly to the left ,
speaker: B
text: yeah .
speaker: A
text: The um <disfmarker> What was <disfmarker> Was this number <pause> forty
speaker: A
text: or <disfmarker> It was roughly the same as this one , <pause> he said ?
speaker: B
text: Um .
speaker: A
text: When you had the two language versus the three language ?
speaker: B
text: That 's what he was saying .
speaker: A
text: That 's where he removed English ,
speaker: F
text: Yeah .
speaker: A
text: right ?
speaker: B
text: Right .
speaker: F
text: It sometimes , actually , depends on what features you 're using .
speaker: B
text: Yeah .
speaker: B
text: But <disfmarker> but i it sounds like <disfmarker>
speaker: F
text: Um , but <disfmarker> <vocalsound> <vocalsound> He <disfmarker> Mm - hmm .
speaker: B
text: I mean . That 's interesting
speaker: B
text: because <pause> it <disfmarker> it seems like what it 's saying is not so much that you got hurt <pause> uh because <pause> you <pause> uh didn't have so much representation of English ,
speaker: B
text: because in the other case you don't get hurt any more ,
speaker: B
text: at least when <pause> it seemed like uh it <disfmarker> it might simply be a case that you have something that is just much more diverse ,
speaker: A
text: Mm - hmm .
speaker: B
text: but you have the same number of parameters representing it .
speaker: A
text: Mm - hmm .
speaker: A
text: I wonder <disfmarker>
speaker: A
text: were um all three of these nets <pause> using the same output ?
speaker: A
text: This multi - language <pause> uh labelling ?
speaker: F
text: He was using uh sixty - four phonemes from <pause> SAMPA .
speaker: A
text: OK , OK .
speaker: F
text: Yeah .
speaker: A
text: So this would <disfmarker> <pause> From this you would say , " well , it doesn't really matter if we put Finnish <pause> into <pause> the training of the neural net , <pause> if there 's <pause> gonna be , <pause> you know , Finnish in the test data . "
speaker: A
text: Right ?
speaker: B
text: Well , it 's <disfmarker> it sounds <disfmarker> <pause> I mean , we have to be careful ,
speaker: B
text: cuz we haven't gotten a good result yet .
speaker: A
text: Yeah .
speaker: B
text: And comparing different bad results can be <pause> tricky .
speaker: A
text: Hmm .
speaker: B
text: But I <disfmarker> I <disfmarker> I <disfmarker> <pause> I think it does suggest that it 's not so much uh <pause> uh cross <pause> language as cross type of speech .
speaker: A
text: Mm - hmm .
speaker: B
text: It 's <disfmarker> it 's
speaker: B
text: um <disfmarker> <vocalsound> But we did <disfmarker>
speaker: B
text: Oh yeah ,
speaker: B
text: the other thing I was asking him , though , is that I think that in the case <disfmarker>
speaker: B
text: Yeah ,
speaker: B
text: you <disfmarker> you do have to be careful
speaker: B
text: because of com compounded results .
speaker: B
text: I think we got some earlier results <pause> in which you trained on one language and tested on another
speaker: B
text: and you didn't have <pause> three , but you just had one <pause> language .
speaker: B
text: So you trained on <pause> one type of digits and tested on another .
speaker: B
text: Didn - Wasn't there something of that ?
speaker: B
text: Where you , <pause> say , trained on Spanish and tested on <disfmarker> on TI - digits ,
speaker: B
text: or the other way around ?
speaker: B
text: Something like that ?
speaker: E
text: No .
speaker: B
text: I thought there was something like that , <pause> that he showed me <pause> last week .
speaker: B
text: We 'll have to wait till
speaker: B
text: we get <disfmarker>
speaker: A
text: Yeah , that would be interesting .
speaker: B
text: Um , This may have been what I was asking before , Stephane ,
speaker: B
text: but <disfmarker> <pause> but , um , wasn't there something that you did , <pause> where you trained <pause> on one language and tested on another ?
speaker: B
text: I mean no <disfmarker> no mixture but just <disfmarker>
speaker: F
text: I 'll get it for you .
speaker: D
text: Uh , no , no .
speaker: B
text: We 've never just trained on one lang
speaker: D
text: Training on a single language , you mean ,
speaker: D
text: and testing on the other one ?
speaker: B
text: Yeah .
speaker: E
text: Not yet .
speaker: D
text: Uh , no .
speaker: D
text: So the only <pause> task that 's similar to this is the training on two languages ,
speaker: D
text: and <comment> that <disfmarker>
speaker: B
text: But we 've done a bunch of things where we just trained on one language .
speaker: B
text: Right ?
speaker: B
text: I mean , you haven't <disfmarker> you haven't done all your tests on multiple languages .
speaker: D
text: Uh , No .
speaker: D
text: Either thi this is test with <pause> uh the same language <pause> but from the broad data ,
speaker: D
text: or it 's test with <pause> uh different languages
speaker: D
text: also from the broad data ,
speaker: D
text: excluding the <disfmarker>
speaker: E
text: The early experiment that <disfmarker>
speaker: D
text: So , it 's <disfmarker> it 's three or <disfmarker> three and four .
speaker: A
text: Did you do different languages from digits ?
speaker: D
text: Uh . No .
speaker: D
text: You mean <pause> training digits <pause> on one language and using the net <pause> to recognize on the other ?
speaker: A
text: Digits on another language ?
speaker: D
text: No .
speaker: B
text: See , I thought you showed me something like that last week .
speaker: B
text: You had a <disfmarker> you had a little <disfmarker>
speaker: D
text: Uh , <pause> No , I don't think so .
speaker: B
text: Um What <disfmarker>
speaker: C
text: These numbers are uh <pause> ratio to baseline ?
speaker: B
text: So , I mean wha what 's the <disfmarker>
speaker: D
text: So .
speaker: B
text: This <disfmarker> this chart <disfmarker> this table that we 're looking at <pause> is um , show is all testing for TI - digits ,
speaker: B
text: or <disfmarker> ?
speaker: F
text: Bigger is worse .
speaker: D
text: So you have uh basically two <pause> uh parts .
speaker: F
text: This is error rate , I think .
speaker: C
text: Ratio .
speaker: F
text: No . <pause> No .
speaker: D
text: The upper part is for TI - digits
speaker: F
text: Yeah , yeah , yeah .
speaker: D
text: and it 's divided in three <pause> rows <pause> of four <disfmarker> four rows each .
speaker: F
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: D
text: And the first four rows is well - matched ,
speaker: D
text: then the s the second group of four rows is mismatched ,
speaker: D
text: and <pause> finally highly mismatched .
speaker: D
text: And then the lower part is for Italian
speaker: D
text: and it 's the same <disfmarker> <pause> the same thing .
speaker: A
text: So , so the upper part is training <pause> TI - digits ?
speaker: D
text: So . It 's <disfmarker> it 's the HTK results , I mean .
speaker: D
text: So it 's <pause> HTK training testings <pause> with different kind of features
speaker: A
text: Ah .
speaker: D
text: and what appears in the <pause> uh left column is <pause> the networks that are used for doing this .
speaker: B
text: Hmm .
speaker: D
text: So . Uh Yeah .
speaker: B
text: Well , What was is that i What was it that you had <pause> done <pause> last week when you showed <disfmarker>
speaker: B
text: Do you remember ?
speaker: B
text: Wh - when you showed me <pause> the <disfmarker> your table last week ?
speaker: D
text: It - It was part of these results .
speaker: D
text: Mmm .
speaker: D
text: Mmm .
speaker: A
text: So where is the baseline <pause> for the TI - digits <pause> located in here ?
speaker: D
text: You mean the HTK Aurora baseline ?
speaker: A
text: Yeah .
speaker: D
text: It 's uh the one hundred number .
speaker: D
text: It 's , well , all these numbers are the ratio <pause> with respect to the baseline .
speaker: A
text: Ah !
speaker: A
text: Ah , OK , OK .
speaker: B
text: So this is word <disfmarker> word error rate ,
speaker: B
text: so a high number is bad .
speaker: D
text: Yeah , this is <pause> a word error rate ratio .
speaker: E
text: Yeah .
speaker: D
text: Yeah .
speaker: A
text: OK , I see .
speaker: D
text: So , seventy point two means that <pause> we reduced the error rate uh by thirty <disfmarker> thirty percent .
speaker: D
text: So .
speaker: A
text: OK , OK , gotcha .
speaker: D
text: Hmm .
speaker: B
text: OK , <vocalsound> so if we take
speaker: B
text: uh um
speaker: B
text: let 's see
speaker: B
text: PLP <pause> uh with on - line <pause> normalization and <pause> delta - del
speaker: B
text: so that 's this thing you have circled here <pause> in the second column ,
speaker: D
text: Yeah .
speaker: B
text: um <pause> and " multi - English " refers to what ?
speaker: D
text: To TIMIT .
speaker: D
text: Mmm .
speaker: D
text: Then you have <pause> uh MF , <pause> MS and ME
speaker: D
text: which are for French , Spanish and English .
speaker: D
text: And ,
speaker: D
text: yeah .
speaker: D
text: Actually I <disfmarker> <pause> I uh forgot to say that <pause> the multilingual net are trained <pause> on <pause> uh <pause> features without the s derivatives
speaker: D
text: uh but with <pause> increased frame numbers .
speaker: D
text: Mmm .
speaker: D
text: And we can <disfmarker> we can see on the first line of the table that it <disfmarker> it <disfmarker> <pause> it 's slightly <disfmarker> slightly worse when we don't use
speaker: D
text: delta but it 's not <disfmarker> <pause> not that much .
speaker: B
text: Right .
speaker: B
text: So w w So , I 'm sorry . I missed that .
speaker: B
text: What 's MF , MS and ME ?
speaker: A
text: Multi - French ,
speaker: D
text: So . Multi - French , Multi - Spanish , and Multi - English .
speaker: A
text: Multi - Spanish
speaker: B
text: Uh OK .
speaker: B
text: So , it 's <pause> uh <pause> broader vocabulary .
speaker: D
text: Yeah .
speaker: B
text: Then <disfmarker> And <disfmarker>
speaker: B
text: OK
speaker: B
text: so I think what I 'm <disfmarker> what I saw in your smaller chart that I was thinking of was <disfmarker> was <pause> there were some numbers I saw , I think , that included these multiple languages
speaker: B
text: and it <disfmarker> and I was seeing <pause> that it got worse .
speaker: B
text: I <disfmarker> I think that was all it was .
speaker: B
text: You had some very limited results that <disfmarker> at that point
speaker: D
text: Yeah .
speaker: B
text: which showed <pause> having in these <disfmarker> these other languages .
speaker: B
text: In fact it might have been just this last category , <pause> having two languages broad that were <disfmarker> where <disfmarker> where English was removed .
speaker: B
text: So that was cross language
speaker: B
text: and the <disfmarker> and the result was quite poor .
speaker: B
text: What I <disfmarker> <pause> we hadn't seen yet was that if you added in the English , it 's still poor .
speaker: D
text: Yeah .
speaker: B
text: Uh <vocalsound> <vocalsound> Um now , what 's the noise condition <pause> um <pause> of the training data <disfmarker>
speaker: D
text: Still poor .
speaker: B
text: Well , I think this is what you were explaining .
speaker: B
text: The noise condition is the same <disfmarker>
speaker: B
text: It 's the same
speaker: B
text: uh Aurora noises uh , in all these cases <pause> for the training .
speaker: D
text: Yeah . Yeah .
speaker: B
text: So there 's not a <pause> statistical <disfmarker> sta a strong st <pause> statistically different <pause> noise characteristic between <pause> uh the training and test
speaker: D
text: No these are the s s s same noises ,
speaker: B
text: and yet we 're seeing some kind of effect <disfmarker>
speaker: D
text: yeah .
speaker: D
text: At least <disfmarker> at least for the first <disfmarker> <pause> for the well - matched ,
speaker: F
text: Well matched condition .
speaker: D
text: yeah .
speaker: B
text: Right .
speaker: B
text: So there 's some kind of a <disfmarker> a <disfmarker> an effect from having these <disfmarker> uh this broader coverage
speaker: B
text: um
speaker: B
text: Now I guess what we should try doing with this is try <pause> testing these on u this same sort of thing
speaker: B
text: on <disfmarker>
speaker: B
text: you probably must have this <pause> lined up to do .
speaker: B
text: To try the same t <pause> with the exact same training , do testing on <pause> the other languages .
speaker: D
text: Mmm .
speaker: B
text: On <disfmarker> on um <disfmarker>
speaker: B
text: So . Um ,
speaker: B
text: oh I well , wait a minute .
speaker: B
text: You have this here , for the Italian .
speaker: B
text: That 's right .
speaker: D
text: Yeah .
speaker: B
text: OK ,
speaker: B
text: so , <pause> So .
speaker: D
text: Yeah , so for the Italian the results are <vocalsound> uh <pause> stranger
speaker: D
text: um <pause> Mmm .
speaker: D
text: So what appears is that perhaps Spanish is <pause> not very close to Italian
speaker: D
text: because uh , well , <pause> when using the <disfmarker> the network trained only on Spanish it 's <disfmarker> <pause> the error rate is <pause> almost uh twice <pause> the baseline error rate .
speaker: B
text: Mm - hmm .
speaker: D
text: Mmm . <vocalsound> Uh .
speaker: B
text: Well , I mean , let 's see .
speaker: B
text: Is there any difference in <disfmarker>
speaker: B
text: So it 's in <pause> the uh <disfmarker>
speaker: B
text: So you 're saying that <pause> when you train on English <pause> and <pause> uh <pause> and <disfmarker> and test on <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: No , you don't have training on English testing <disfmarker>
speaker: D
text: There <disfmarker> there is <disfmarker> another difference , is that the noise <disfmarker> the noises are different .
speaker: D
text: Well , For <disfmarker> for the Italian part I mean
speaker: B
text: In <disfmarker> in what ?
speaker: D
text: the <pause> uh <pause> the um <pause> networks are trained with noise from <pause> Aurora <disfmarker> TI - digits ,
speaker: E
text: Aurora - two .
speaker: D
text: mmm .
speaker: D
text: Yeah .
speaker: B
text: And the noise is different in th
speaker: D
text: And perhaps the noise are <pause> quite different from the noises <pause> in the speech that Italian .
speaker: B
text: Do we have any um <pause> test sets <pause> uh in <pause> any other language that um have the same noise as in <pause> the Aurora ?
speaker: D
text: And <disfmarker>
speaker: E
text: Mmm , no .
speaker: D
text: No .
speaker: A
text: Can I ask something real quick ?
speaker: A
text: In <disfmarker> in the upper part <disfmarker> <pause> in the English <pause> stuff , <pause> it looks like the very best number is sixty point nine ?
speaker: A
text: and that 's in the uh <disfmarker> <pause> the third <pause> section in the upper part under PLP JRASTA ,
speaker: A
text: sort of the middle column ?
speaker: D
text: Yeah .
speaker: A
text: I is that <pause> a noisy condition ?
speaker: D
text: Yeah .
speaker: A
text: So that 's matched training ?
speaker: A
text: Is that what that is ?
speaker: D
text: It 's <disfmarker>
speaker: D
text: no ,
speaker: D
text: the third part ,
speaker: D
text: so it 's uh <pause> highly mismatched .
speaker: D
text: So . Training and <pause> test noise are different .
speaker: A
text: So <disfmarker> why do you get your best number in <disfmarker>
speaker: A
text: Wouldn't you get your best number in the clean case ?
speaker: C
text: Well , it 's relative to the um <pause> baseline mismatching
speaker: D
text: Yeah .
speaker: A
text: Ah ,
speaker: D
text: Yeah . Yeah .
speaker: A
text: OK
speaker: A
text: so these are not <disfmarker>
speaker: A
text: OK ,
speaker: A
text: alright , I see .
speaker: C
text: Yeah .
speaker: A
text: OK .
speaker: A
text: And then <disfmarker> so , in the <disfmarker> in the um <disfmarker> <pause> in the <pause> non - mismatched clean case , <pause> your best one was under MFCC ?
speaker: A
text: That sixty - one point four ?
speaker: D
text: Yeah . <pause> But it 's not a clean case .
speaker: D
text: It 's <pause> a noisy case
speaker: D
text: but <pause> uh training and test noises are the same .
speaker: A
text: Oh ! So this upper third ?
speaker: D
text: So <disfmarker> Yeah .
speaker: A
text: Uh that 's still noisy ?
speaker: D
text: Yeah .
speaker: A
text: Ah , OK .
speaker: D
text: So it 's always noisy basically ,
speaker: A
text: Mm - hmm .
speaker: D
text: and , <pause> well , the <disfmarker>
speaker: A
text: I see .
speaker: D
text: Mmm .
speaker: B
text: OK ?
speaker: B
text: Um <pause> So uh ,
speaker: B
text: I think this will take some <pause> looking at , thinking about .
speaker: B
text: But , <pause> what is uh <disfmarker> what is currently running , that 's <disfmarker> uh , i that <disfmarker> just filling in the holes here
speaker: B
text: or <disfmarker> or <disfmarker> ? <comment> <pause> pretty much ?
speaker: D
text: Uh , no we don't plan to fill the holes
speaker: D
text: but <pause> actually there is something important ,
speaker: B
text: OK .
speaker: D
text: is that <pause> um we made a lot of assumption concerning the on - line normalization
speaker: D
text: and we just noticed <pause> uh recently that <pause> uh the <pause> approach that we were using <pause> was not <pause> uh <pause> leading to very good results <pause> when we <pause> used the straight features to HTK .
speaker: D
text: Um <pause> <pause> Mmm .
speaker: D
text: So basically d <pause> if you look at the <disfmarker> at the left of the table , <pause> the first uh row , <pause> with eighty - six , one hundred , and forty - three and seventy - five ,
speaker: D
text: these are the results we obtained for Italian <pause> uh with <pause> straight <pause> mmm , PLP features <pause> using on - line normalization .
speaker: B
text: Mm - hmm .
speaker: D
text: Mmm . And the , mmm <disfmarker> what 's <pause> in the table , just <pause> at the left of the PLP twelve <pause> on - line normalization column ,
speaker: D
text: so , the numbers seventy - nine , fifty - four and <pause> uh forty - two <pause> are the results obtained by uh Pratibha with <pause> uh his on - line normalization <disfmarker> uh her on - line normalization approach .
speaker: A
text: Where is that ?
speaker: A
text: seventy - nine , fifty
speaker: B
text: Uh , it 's just sort of sitting right on the uh <disfmarker> the column line .
speaker: E
text: Fifty - one ?
speaker: D
text: So .
speaker: E
text: This <disfmarker>
speaker: B
text: Uh . <pause> Yeah .
speaker: A
text: Oh I see , OK .
speaker: D
text: Just <disfmarker> uh
speaker: D
text: Yeah .
speaker: D
text: So these are the results of <pause> OGI with <pause> on - line normalization
speaker: D
text: and straight features to HTK .
speaker: D
text: And the previous result , eighty - six and so on , <pause> are with our <pause> features straight to HTK .
speaker: B
text: Yes .
speaker: B
text: Yes .
speaker: D
text: So <pause> what we see that <disfmarker> is <disfmarker> there is that um <pause> uh the way we were doing this was not correct ,
speaker: D
text: but <pause> still <pause> the networks <pause> are very good .
speaker: D
text: When we use the networks <pause> our number are better that <pause> uh Pratibha results .
speaker: E
text: We improve .
speaker: B
text: So , do you know what was wrong with the on - line normalization , or <disfmarker> ?
speaker: D
text: Yeah .
speaker: D
text: There were diff there were different things
speaker: D
text: and <pause> basically , <pause> the first thing is the mmm , <pause> alpha uh <pause> value .
speaker: D
text: So , the recursion <pause> uh <pause> part .
speaker: D
text: um , <pause> I used point five percent , <pause> which was the default value in the <disfmarker> <pause> in the programs here .
speaker: D
text: And Pratibha used five percent .
speaker: B
text: Uh
speaker: D
text: So it adapts more <pause> quickly
speaker: B
text: Yes .
speaker: B
text: Yeah .
speaker: D
text: Um , but ,
speaker: D
text: yeah .
speaker: D
text: I assume that this was not important because <pause> uh previous results from <disfmarker> from Dan and <disfmarker> show that basically <pause> the <pause> both <disfmarker> both values g give the same <disfmarker> same <pause> uh results .
speaker: D
text: It was true on uh <pause> TI - digits but it 's not true on Italian .
speaker: B
text: Mm - hmm .
speaker: D
text: Uh , second thing is the initialization of the <pause> stuff .
speaker: D
text: Actually , <pause> uh what we were doing is to start the recursion from the beginning of the <pause> utterance .
speaker: D
text: And using initial values that are the global mean and variances <pause> measured across the whole database .
speaker: B
text: Right .
speaker: B
text: Right .
speaker: D
text: And Pratibha did something different is
speaker: D
text: that he <disfmarker> uh she initialed the um values of the mean and variance <pause> by computing <pause> this on the <pause> twenty - five first frames of each utterance .
speaker: D
text: Mmm . There were other minor differences ,
speaker: D
text: the fact that <pause> she used fifteen dissities instead s instead of thirteen ,
speaker: D
text: and that she used C - zero instead of log energy .
speaker: D
text: Uh , but the main differences concerns the recursion .
speaker: D
text: So . <pause> Uh , I changed the code
speaker: D
text: uh and now we have a baseline that 's similar to the OGI baseline .
speaker: B
text: OK .
speaker: D
text: We <disfmarker> It <disfmarker> it 's slightly <pause> uh different
speaker: D
text: because <pause> I don't exactly initialize the same way she does .
speaker: D
text: Actually I start , <pause> mmm , I don't wait to a fifteen <disfmarker> twenty - five <disfmarker> twenty - five frames <pause> before computing a mean and the variance <pause> to e to <disfmarker> to start the recursion .
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: D
text: I <disfmarker> I use the on - line scheme
speaker: D
text: and only start the re recursion after the twenty - five <disfmarker> <pause> twenty - fifth frame .
speaker: D
text: But , well it 's similar .
speaker: D
text: So <pause> uh I retrained <pause> the networks with <pause> these <disfmarker>
speaker: D
text: well , the <disfmarker> the <disfmarker> the networks are retaining with these new <pause> features .
speaker: B
text: Mm - hmm .
speaker: D
text: And ,
speaker: D
text: yeah .
speaker: B
text: OK .
speaker: D
text: So basically what I expect is that <pause> these numbers will a little bit go down
speaker: D
text: but <pause> perhaps not <disfmarker> not so much
speaker: B
text: Right .
speaker: D
text: because <pause> I think the neural networks learn perhaps <pause> to <disfmarker>
speaker: D
text: even if the features are not <pause> normalized . It <disfmarker> it will learn how to normalize
speaker: B
text: Right .
speaker: D
text: and <disfmarker>
speaker: B
text: OK , but I think that <pause> given the pressure of time we probably want to draw <disfmarker> because of that <pause> especially , we wanna draw some conclusions from this ,
speaker: B
text: do some reductions <pause> in what we 're looking at ,
speaker: D
text: Yeah .
speaker: B
text: and make some strong decisions for what we 're gonna do testing on before next week .
speaker: D
text: Yeah <vocalsound> I
speaker: B
text: So do you <disfmarker> are you <disfmarker> w did you have something going on , on the side , with uh multi - band <pause> or <disfmarker> on <disfmarker> on this ,
speaker: D
text: No ,
speaker: B
text: or <disfmarker> ?
speaker: D
text: I <disfmarker> we plan to start this
speaker: D
text: uh so , act actually we have discussed uh <pause> @ @ um , these <disfmarker>
speaker: D
text: what we could do <pause> more as a <disfmarker> as a research
speaker: D
text: and <disfmarker> <pause> and <pause> we were thinking perhaps that <pause> uh <pause> the way we use the tandem is not <disfmarker>
speaker: D
text: Uh , well , there is basically perhaps a flaw in the <disfmarker> in the <disfmarker> the stuff
speaker: D
text: because <pause> we <pause> trained the networks <disfmarker>
speaker: D
text: If we trained the networks on the <disfmarker> on <pause> a language and a t or a specific <pause> task ,
speaker: B
text: Mm - hmm .
speaker: D
text: um , what we ask is <disfmarker> to the network <disfmarker> is to put the bound the decision boundaries somewhere in the space .
speaker: B
text: Mmm .
speaker: D
text: And uh <pause> mmm and ask the network to put one , <pause> at one side of the <disfmarker> for <disfmarker> for a particular phoneme at one side of the boundary <disfmarker> decision boundary
speaker: D
text: and one for another phoneme at the other side .
speaker: D
text: And <pause> so there is kind of reduction of the information there that 's not correct
speaker: D
text: because if we change task <pause> and if the phonemes are not in the same context in the new task , <pause> obviously the <pause> decision boundaries are not <disfmarker> <pause> should not be at the same <pause> place .
speaker: D
text: But the way the feature gives <disfmarker> The <disfmarker> the way the network gives the features is that it reduce completely the <disfmarker> <pause> it removes completely the information <disfmarker> <pause> a lot of information from the <disfmarker> the features <pause> by uh <pause> uh <pause> placing the decision boundaries at <pause> optimal places for <pause> one kind of <pause> data
speaker: B
text: I di
speaker: D
text: but <pause> this is not the case for another kind of data .
speaker: B
text: It 's a trade - off ,
speaker: D
text: So <disfmarker>
speaker: B
text: right ?
speaker: B
text: Any - anyway go ahead .
speaker: D
text: Yeah .
speaker: D
text: So uh what we were thinking about is perhaps <pause> um one way <pause> to solve this problem is increase the number of <pause> outputs of the neural networks .
speaker: D
text: Doing something like , um <pause> um phonemes within context and ,
speaker: D
text: well , basically context dependent phonemes .
speaker: B
text: Maybe .
speaker: B
text: I mean , I <disfmarker> I think <pause> you could make <pause> the same argument ,
speaker: B
text: it 'd be just as legitimate , <pause> for hybrid systems <pause> as well .
speaker: D
text: Yeah but , we know that <disfmarker>
speaker: B
text: Right .
speaker: B
text: And in fact , <pause> th things get better with context dependent <pause> versions .
speaker: B
text: Right ?
speaker: D
text: Ye - yeah but here it 's something different .
speaker: D
text: We want to have features
speaker: D
text: uh well , <pause> um .
speaker: B
text: Yeah .
speaker: B
text: Yeah , but it 's still true <pause> that what you 're doing <pause> is
speaker: B
text: you 're ignoring <disfmarker>
speaker: B
text: you 're <disfmarker> you 're coming up with something to represent , <pause> whether it 's a distribution , <pause> probability distribution or features ,
speaker: B
text: you 're coming up with a set of variables <pause> that are representing <pause> uh , <pause> things that vary w over context .
speaker: D
text: Mm - hmm .
speaker: B
text: Uh , and you 're <pause> putting it all together ,
speaker: B
text: ignoring the differences in context .
speaker: B
text: That <disfmarker> that 's true <pause> for the hybrid system ,
speaker: B
text: it 's true for a tandem system .
speaker: B
text: So , for that reason , when you <disfmarker> in <disfmarker> in <disfmarker> in a hybrid system , <pause> when you incorporate context one way or another , <pause> you do get better scores .
speaker: D
text: Yeah .
speaker: B
text: OK ?
speaker: B
text: But I <disfmarker> it 's <disfmarker> it 's a big deal <pause> to get that .
speaker: B
text: I <disfmarker> I 'm <disfmarker> I 'm sort of <disfmarker>
speaker: B
text: And once you <disfmarker> the other thing is that once you represent <disfmarker> start representing more and more context <pause> it is <pause> uh <pause> much more <pause> um specific <pause> to a particular task in language .
speaker: B
text: So um Uh , the <disfmarker> <pause> the acoustics associated with <pause> uh a particular context ,
speaker: B
text: for instance you may have some kinds of contexts that will never occur <pause> in one language and will occur frequently in the other ,
speaker: B
text: so the qu
speaker: B
text: the issue of getting enough training <pause> for a particular kind of context becomes harder .
speaker: B
text: We already actually don't have a huge amount of training data
speaker: B
text: um
speaker: D
text: Yeah , but <disfmarker> mmm , I mean , <pause> the <disfmarker>
speaker: D
text: the way we <disfmarker> we do it now is that we have a neural network
speaker: D
text: and <pause> basically <pause> the net network is trained almost to give binary decisions .
speaker: B
text: Right .
speaker: D
text: And <pause> uh <disfmarker> binary decisions about phonemes .
speaker: D
text: Nnn <disfmarker> Uh It 's <disfmarker>
speaker: B
text: Almost .
speaker: B
text: But I mean it <disfmarker> it <disfmarker> it does give a distribution .
speaker: D
text: Yeah .
speaker: B
text: It 's <disfmarker> and <disfmarker> and <pause> it is true that if there 's two phones that are very similar , <pause> that <pause> uh <pause> the <disfmarker> <pause> i it may prefer one but it will <pause> give a reasonably high value to the other , too .
speaker: D
text: Yeah .
speaker: D
text: Yeah , sure
speaker: D
text: but
speaker: D
text: uh <pause> So basically it 's almost binary decisions
speaker: D
text: and <pause> um the idea of using more <pause> classes is <pause> to <pause> get something that 's <pause> less binary decisions .
speaker: B
text: Oh no ,
speaker: B
text: but it would still be even more of a binary decision .
speaker: B
text: It <disfmarker> it 'd be even more of one .
speaker: B
text: Because then you would say <pause> that in <disfmarker> that this phone in this context is a one , <pause> but the same phone in a slightly different context is a zero .
speaker: D
text: But <disfmarker> yeah , but <disfmarker>
speaker: B
text: That would be even <disfmarker> even more distinct of a binary decision .
speaker: B
text: I actually would have thought you 'd wanna go the other way and have fewer classes .
speaker: D
text: Yeah , but if <disfmarker>
speaker: B
text: Uh , I mean for instance , the <disfmarker> the thing I was arguing for before , but again which I don't think we have time to try , <pause> is something in which you would modify the code so you could train to have several outputs on and use articulatory features
speaker: D
text: Mmm .
speaker: D
text: Mm - hmm .
speaker: B
text: cuz then that would <disfmarker> that would go <disfmarker> <pause> that would be much broader and cover many different situations .
speaker: B
text: But if you go to very very fine categories , it 's very <pause> binary .
speaker: D
text: Mmm .
speaker: D
text: Yeah , but I think <disfmarker>
speaker: D
text: Yeah , perhaps you 're right ,
speaker: D
text: but you have more classes
speaker: D
text: so <pause> you <disfmarker> you have more information in your features .
speaker: D
text: So , <vocalsound> Um <pause> You have more information in the <pause> uh
speaker: B
text: Mm - hmm .
speaker: B
text: True .
speaker: D
text: posteriors vector
speaker: D
text: um
speaker: D
text: which means that <disfmarker>
speaker: D
text: But still the information is relevant
speaker: B
text: Mm - hmm .
speaker: D
text: because it 's <disfmarker> it 's information that helps to discriminate ,
speaker: B
text: Mm - hmm .
speaker: D
text: if it 's possible to be able to discriminate <pause> among the phonemes in context .
speaker: B
text: Well it 's <disfmarker>
speaker: D
text: But the <disfmarker>
speaker: B
text: it 's <disfmarker> <pause> it 's an interesting thought .
speaker: B
text: I mean we <disfmarker> we could disagree about it at length
speaker: D
text: Mmm .
speaker: D
text: Mmm .
speaker: B
text: but the <disfmarker> the real thing is if you 're interested in it you 'll probably try it
speaker: B
text: and <disfmarker> <pause> and <pause> we 'll see .
speaker: B
text: But <disfmarker> but what I 'm more concerned with now , as an operational level , is <pause> uh , you know ,
speaker: D
text: Mmm .
speaker: B
text: what do we do in four or five days ?
speaker: B
text: Uh , and <disfmarker> <pause> so we have <pause> to be concerned <pause> with
speaker: B
text: Are we gonna look at any combinations of things ,
speaker: B
text: you know once the nets get retrained so you have this problem out of it .
speaker: D
text: Mmm .
speaker: B
text: Um , are we going to look at <pause> multi - band ?
speaker: B
text: Are we gonna look at combinations of things ?
speaker: B
text: Uh , what questions are we gonna ask ,
speaker: B
text: uh now that ,
speaker: B
text: I mean , <pause> we should probably turn shortly to this O G I note .
speaker: B
text: Um , how are we going to <pause> combine <pause> with what they 've been focusing on ?
speaker: B
text: Uh , <pause> Uh we haven't been doing any of the L D A RASTA sort of thing .
speaker: D
text: Mm - hmm .
speaker: B
text: And they , although they don't talk about it in this note ,
speaker: B
text: um , <pause> there 's um , <pause> the issue of the <pause> um Mu law <pause> business <pause> uh <pause> versus the logarithm ,
speaker: D
text: Mm - hmm .
speaker: B
text: um , <pause> so .
speaker: B
text: So what i what is going on right now ?
speaker: B
text: What 's
speaker: B
text: right <disfmarker>
speaker: B
text: you 've got <pause> nets retraining ,
speaker: B
text: Are there <disfmarker> is there <disfmarker> are there any H T K <pause> trainings <disfmarker> testings going on ?
speaker: D
text: N
speaker: E
text: I <disfmarker> I <disfmarker> I 'm trying the HTK with eh , <pause> PLP twelve on - line delta - delta and MSG filter <pause> together .
speaker: B
text: The combination ,
speaker: B
text: I see .
speaker: E
text: The combination , yeah .
speaker: E
text: But I haven't result <vocalsound> at this moment .
speaker: B
text: MSG and <disfmarker> and PLP .
speaker: E
text: Yeah .
speaker: B
text: And is this with the revised <pause> on - line normalization ?
speaker: E
text: Ye - Uh , with the old <pause> older ,
speaker: D
text: Yeah .
speaker: B
text: Old one .
speaker: E
text: yeah .
speaker: B
text: So it 's using all the nets for that
speaker: B
text: but again we have the hope that it <disfmarker> <pause> We have the hope that it <disfmarker> <pause> maybe it 's not making too much difference ,
speaker: E
text: Yeah .
speaker: E
text: But <pause> We can
speaker: E
text: know soon .
speaker: E
text: Maybe .
speaker: B
text: but <disfmarker> but
speaker: B
text: yeah .
speaker: E
text: I don't know .
speaker: D
text: Yeah .
speaker: B
text: Uh , OK .
speaker: D
text: Uh so there is this combination ,
speaker: D
text: yeah .
speaker: D
text: Working on combination obviously .
speaker: E
text: Mm - hmm .
speaker: D
text: Um , I will start work on multi - band .
speaker: D
text: And <pause> we <pause> plan to work also on the idea of using both <pause> features <pause> and net outputs .
speaker: D
text: Um . And <pause> we think that <pause> with this approach perhaps <pause> we could reduce the number of outputs of the neural network .
speaker: D
text: Um , So , get simpler networks ,
speaker: D
text: because we still have the features .
speaker: D
text: So we have um <pause> come up with um <pause> different kind of <pause> broad phonetic categories .
speaker: D
text: And we have <disfmarker> Basically we have three <pause> types of broad phonetic classes .
speaker: D
text: Well , something using place of articulation
speaker: D
text: which <disfmarker> which leads to <pause> nine , I think , <pause> broad classes .
speaker: D
text: Uh , another which is based on manner ,
speaker: D
text: which is <disfmarker> is also something like nine classes .
speaker: D
text: And then , <pause> something that combine both ,
speaker: D
text: and we have <pause> twenty f <pause> twenty - five ?
speaker: F
text: Twenty - seven .
speaker: D
text: Twenty - seven broad classes .
speaker: D
text: So like , uh ,
speaker: D
text: oh , I don't know ,
speaker: D
text: like back vowels , front vowels .
speaker: B
text: So what you do <disfmarker>
speaker: D
text: Um For the moments we do not <disfmarker> don't have nets ,
speaker: B
text: um I just wanna understand
speaker: B
text: so <pause> You have two net or three nets ?
speaker: B
text: Was this ?
speaker: B
text: How many <disfmarker> how many nets do you have ?
speaker: D
text: I mean , <pause> It 's just <disfmarker> Were we just changing <pause> the labels to retrain nets <pause> with fewer out outputs .
speaker: B
text: No nets .
speaker: E
text: Begin to work in this .
speaker: E
text: We are @ @ .
speaker: B
text: Right .
speaker: D
text: And then <disfmarker>
speaker: B
text: But <disfmarker> but I didn't understand <disfmarker>
speaker: D
text: Mm - hmm .
speaker: B
text: Uh . <pause> the software currently just has <disfmarker> uh a <disfmarker> allows for I think , the one <disfmarker> one hot output .
speaker: B
text: So you 're having multiple nets and combining them ,
speaker: B
text: or <disfmarker> ?
speaker: B
text: Uh , how are you <disfmarker> how are you coming up with <disfmarker>
speaker: B
text: If you say <pause> uh <pause> If you have a place <pause> characteristic and a manner characteristic , how do you <disfmarker>
speaker: D
text: It - It 's the single net ,
speaker: A
text: I think they have one output .
speaker: D
text: yeah .
speaker: B
text: Oh , it 's just one net .
speaker: E
text: Yeah .
speaker: F
text: mm - hmm
speaker: D
text: It 's one net with <pause> um <pause> twenty - seven outputs
speaker: D
text: if we have twenty - seven classes ,
speaker: B
text: I see .
speaker: D
text: yeah .
speaker: B
text: I see , OK .
speaker: D
text: So it 's <disfmarker> Well , it 's basically a standard net with fewer <pause> classes .
speaker: B
text: So you 're sort of going the other way of what you were saying a bit ago
speaker: B
text: instead of <disfmarker>
speaker: D
text: Yeah ,
speaker: B
text: yeah .
speaker: D
text: but I think <disfmarker>
speaker: D
text: Yeah .
speaker: F
text: But including the features .
speaker: D
text: B b including the features , yeah .
speaker: E
text: Yeah .
speaker: D
text: I don't think this <pause> will work <pause> alone .
speaker: D
text: I think it will get worse
speaker: D
text: because Well , I believe the effect that <disfmarker> of <disfmarker> of too reducing too much the information is <pause> basically <disfmarker> basically what happens
speaker: B
text: Uh - huh .
speaker: D
text: and <disfmarker>
speaker: B
text: But you think if you include that <pause> plus the other features ,
speaker: D
text: but <disfmarker>
speaker: D
text: Yeah ,
speaker: D
text: because <pause> there is perhaps one important thing that the net <pause> brings ,
speaker: D
text: and OGI show showed that , is <pause> the distinction between <pause> sp speech and silence
speaker: D
text: Because these nets are trained on well - controlled condition .
speaker: D
text: I mean the labels are obtained on clean speech , and we add noise after .
speaker: D
text: So this is one thing
speaker: D
text: And
speaker: D
text: But perhaps , something intermediary using also <pause> some broad classes could <disfmarker> could bring so much more information .
speaker: D
text: Uh .
speaker: B
text: So <disfmarker> so again then we have these broad classes
speaker: B
text: and <disfmarker> well , somewhat broad .
speaker: B
text: I mean , it 's twenty - seven instead of sixty - four , <pause> basically .
speaker: D
text: Yeah .
speaker: B
text: And you have the original features .
speaker: B
text: Which are PLP , or something .
speaker: D
text: Yeah .
speaker: D
text: Mm - hmm .
speaker: B
text: And then uh , just to remind me , all of that goes <pause> into <disfmarker> uh , that all of that is transformed by uh , uh , K - KL or something , or <disfmarker> ?
speaker: D
text: There will probably be ,
speaker: E
text: Mu .
speaker: D
text: yeah ,
speaker: D
text: one single KL to transform everything
speaker: D
text: or <vocalsound> <pause> uh ,
speaker: B
text: Right .
speaker: E
text: No
speaker: E
text: transform the PLP
speaker: D
text: per
speaker: E
text: and only transform the other
speaker: E
text: I 'm not sure .
speaker: D
text: This is <pause> still something <pause> that
speaker: B
text: Well no ,
speaker: B
text: I think <disfmarker>
speaker: D
text: yeah ,
speaker: D
text: we <pause> don't know <disfmarker>
speaker: B
text: I see .
speaker: B
text: So there 's a question of whether you would <disfmarker>
speaker: E
text: Two e @ @ it 's one .
speaker: D
text: Yeah .
speaker: B
text: Right .
speaker: B
text: Whether you would transform together or just one .
speaker: B
text: Yeah .
speaker: B
text: Might wanna try it both ways .
speaker: B
text: But that 's interesting .
speaker: B
text: So that 's something that you 're <disfmarker> you haven't trained yet but are preparing to train , and <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: Um <pause> <pause> Yeah ,
speaker: D
text: Mmm .
speaker: B
text: so I think Hynek will be here Monday .
speaker: B
text: Monday or Tuesday .
speaker: B
text: So
speaker: D
text: Uh , yeah .
speaker: B
text: So I think , you know , we need to <pause> choose the <disfmarker> choose the experiments carefully ,
speaker: B
text: so we can get uh key <disfmarker> <pause> key questions answered <pause> uh before then
speaker: D
text: Mm - hmm .
speaker: B
text: and <pause> leave other ones aside even if it <pause> leaves incomplete <pause> tables <vocalsound> <pause> someplace ,
speaker: B
text: uh <pause> uh , it 's <disfmarker> it 's really time to <disfmarker> <pause> time to choose .
speaker: D
text: Mm - hmm .
speaker: B
text: Um , let me pass this out , <pause> by the way .
speaker: B
text: Um These are <disfmarker>
speaker: B
text: Did <disfmarker> did <disfmarker> <pause> did I interrupt you ?
speaker: E
text: Yeah , I have one .
speaker: B
text: Were there other things that you wanted to <disfmarker>
speaker: D
text: Uh , no . I don't think so .
speaker: D
text: Yeah , I have one .
speaker: G
text: Oh , thanks .
speaker: B
text: Ah ! <pause> OK . <pause> OK , we have <pause> lots of them .
speaker: E
text: We have one .
speaker: B
text: OK , so <vocalsound> um , Something I asked <disfmarker>
speaker: B
text: So they 're <disfmarker> they 're doing <pause> the <disfmarker> the VAD
speaker: B
text: I guess they mean voice activity detection So again , it 's the silence <disfmarker>
speaker: B
text: So they 've just trained up a net <pause> which has two outputs , I believe .
speaker: B
text: Um <vocalsound> I asked uh <pause> Hynek whether <disfmarker>
speaker: B
text: I haven't talked to Sunil <disfmarker>
speaker: B
text: I asked Hynek whether <pause> they compared that to <pause> just taking the nets we already had <pause> and summing up the probabilities .
speaker: D
text: Mm - hmm .
speaker: B
text: Uh . <pause> To get the speech <disfmarker> voice activity detection , or else just using the silence , <pause> if there 's only one <pause> silence output .
speaker: B
text: Um <pause> And , he didn't think they had ,
speaker: B
text: um . But on the other hand , maybe they can get by with a smaller net
speaker: B
text: and <pause> maybe <pause> sometimes you don't run the other ,
speaker: B
text: maybe there 's a computational advantage to having a separate net , anyway .
speaker: D
text: Mm - hmm .
speaker: B
text: So um Their uh <disfmarker> <pause> the results look pretty good .
speaker: D
text: Yeah .
speaker: B
text: Um , <pause> I mean , not uniformly .
speaker: B
text: I mean , there 's a <disfmarker> an example or two <pause> that you can find , where it made it slightly worse , but <pause> uh in <disfmarker> in all but a couple <pause> examples .
speaker: D
text: Mmm .
speaker: B
text: Uh .
speaker: E
text: But they have a question of the result .
speaker: E
text: Um how are trained the <disfmarker> the LDA filter ?
speaker: E
text: How obtained the LDA filter ?
speaker: D
text: Mmm .
speaker: B
text: I I 'm sorry .
speaker: B
text: I don't understand your question .
speaker: E
text: Yes ,
speaker: E
text: um the LDA filter <pause> needs some <pause> training set <pause> to obtain the filter .
speaker: E
text: Maybe
speaker: E
text: I don't know exactly how <pause> they are obtained .
speaker: B
text: It 's on <pause> training .
speaker: E
text: Training , with the training test of each <disfmarker>
speaker: E
text: You understand me ?
speaker: B
text: No .
speaker: E
text: Yeah ,
speaker: E
text: uh for example , <pause> LDA filter <pause> need a set of <disfmarker> <pause> a set of training <pause> to obtain the filter .
speaker: B
text: Yes .
speaker: E
text: And maybe <pause> for the Italian , for the TD <pause> TE on for Finnish , these filter are <disfmarker> are obtained with their own training set .
speaker: B
text: Yes , I don't know .
speaker: B
text: That 's <disfmarker> that 's <disfmarker> so that 's a <disfmarker> that 's a very good question ,
speaker: B
text: then <disfmarker> now that it <disfmarker> <pause> I understand it . It 's
speaker: B
text: " yeah ,
speaker: B
text: where does the LDA come from ? "
speaker: B
text: In the <disfmarker> In <pause> earlier experiments , they had taken LDA <pause> from a completely different database ,
speaker: E
text: Yeah .
speaker: B
text: right ?
speaker: E
text: Yeah , because maybe it the same situation that the neural network training with their own
speaker: D
text: Mmm .
speaker: E
text: set .
speaker: B
text: So that 's a good question .
speaker: B
text: Where does it come from ?
speaker: B
text: Yeah , I don't know .
speaker: B
text: Um , <pause> but uh to tell you the <pause> truth , I wasn't actually looking at the LDA so much when I <disfmarker> I was looking at it
speaker: B
text: I was <pause> mostly thinking about the <disfmarker> <pause> the VAD .
speaker: B
text: And um , it ap <pause> it ap
speaker: B
text: Oh what does <disfmarker> what does ASP ?
speaker: B
text: Oh that 's <disfmarker>
speaker: D
text: The features ,
speaker: D
text: yeah .
speaker: D
text: Yeah .
speaker: E
text: I don't understand also
speaker: B
text: It says " baseline ASP " .
speaker: E
text: what
speaker: E
text: is <disfmarker> <pause> what is the difference between ASP and uh baseline over ?
speaker: D
text: Yeah , I don't know .
speaker: C
text: ASP .
speaker: E
text: This is <disfmarker>
speaker: C
text: Oh .
speaker: B
text: Anybody know <pause> any <disfmarker>
speaker: C
text: There it is .
speaker: B
text: Um Cuz there 's " baseline Aurora " <pause> above it .
speaker: C
text: Mm - hmm .
speaker: B
text: And it 's <disfmarker> This is mostly better than baseline ,
speaker: B
text: although in some cases it 's a little worse , in a couple cases .
speaker: C
text: Well , it says baseline ASP is twenty - three mill <pause> minus thirteen .
speaker: E
text: Yeah .
speaker: B
text: Yeah , it says what it is .
speaker: B
text: But I don't how that 's different <pause> from <disfmarker>
speaker: C
text: From the baseline . <comment> OK .
speaker: B
text: I think this was <disfmarker> <pause> I think this is the same point we were at when <disfmarker> when we were up in Oregon .
speaker: E
text: Yeah .
speaker: D
text: I think <disfmarker> <pause> I think it 's the C - zero <disfmarker> using C - zero instead of log energy .
speaker: E
text: Ah , OK , mm - hmm .
speaker: D
text: Yeah , it 's this .
speaker: E
text: yeah .
speaker: B
text: Oh . OK .
speaker: D
text: It should be that ,
speaker: D
text: yeah .
speaker: D
text: Because <disfmarker>
speaker: B
text: Shouldn't it be <disfmarker>
speaker: A
text: They s they say in here that the VAD is not used as an additional feature .
speaker: A
text: Does <disfmarker> does anybody know how they 're using it ?
speaker: B
text: Yeah . So <disfmarker> so what they 're doing here is , <pause> i
speaker: D
text: Yeah .
speaker: B
text: if you look down at the block diagram , <pause> um , <pause> they estimate <disfmarker> they get a <disfmarker> <pause> they get an estimate <pause> of whether it 's speech or silence ,
speaker: A
text: But that <disfmarker>
speaker: B
text: and then they have a median filter of it .
speaker: A
text: Mm - hmm .
speaker: B
text: And so um , <pause> basically they 're trying to find stretches .
speaker: B
text: The median filter is enforcing a <disfmarker> i it having some continuity .
speaker: A
text: Mm - hmm .
speaker: B
text: You find stretches where the <pause> combination of the <pause> frame wise VAD and the <disfmarker> <pause> the median filter say that there 's a stretch of silence .
speaker: B
text: And then it 's going through and just throwing the data away .
speaker: C
text: Hmm .
speaker: B
text: Right ?
speaker: B
text: So um <disfmarker>
speaker: A
text: So it 's <disfmarker> it 's <disfmarker>
speaker: A
text: I don't understand .
speaker: A
text: You mean it 's throwing out frames ?
speaker: A
text: Before <disfmarker>
speaker: B
text: It 's throwing out chunks of frames , yeah .
speaker: B
text: There 's <disfmarker> the <disfmarker> the median filter is enforcing that it 's not gonna be single cases of frames , or isolated frames .
speaker: A
text: Yeah .
speaker: B
text: So it 's throwing out frames
speaker: B
text: and the thing is <pause> um , <pause> what I don't understand is how they 're doing this with H T
speaker: A
text: Yeah ,
speaker: B
text: This is <disfmarker>
speaker: A
text: that 's what I was just gonna ask .
speaker: A
text: How can you just throw out frames ?
speaker: B
text: Yeah .
speaker: B
text: Well , you <disfmarker> you can ,
speaker: D
text: i
speaker: B
text: right ?
speaker: B
text: I mean y you <disfmarker> you <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: it stretches again .
speaker: B
text: For single frames I think it would be pretty hard .
speaker: A
text: Yeah .
speaker: B
text: But if you say speech starts here , speech ends there .
speaker: A
text: Mm - hmm .
speaker: B
text: Right ?
speaker: C
text: Huh .
speaker: D
text: Yeah .
speaker: D
text: Yeah , you can basically remove the <disfmarker> the frames from the feature <disfmarker> feature files .
speaker: B
text: Yeah .
speaker: B
text: Yeah , so I mean in the <disfmarker> i i in the <disfmarker> in the decoding , you 're saying that we 're gonna decode from here to here .
speaker: D
text: I t
speaker: A
text: Mm - hmm .
speaker: B
text: I think they 're <disfmarker> they 're <disfmarker> they 're treating it , <pause> you know , like uh <disfmarker>
speaker: B
text: well , it 's not isolated word ,
speaker: B
text: but <disfmarker> but connected ,
speaker: B
text: you know , the <disfmarker> the <disfmarker>
speaker: A
text: In the text they say that this <disfmarker> this is a tentative block diagram of a possible configuration we could think of .
speaker: A
text: So that sort of sounds like they 're not doing that yet .
speaker: B
text: Well . <pause> No they <disfmarker> they have numbers though ,
speaker: B
text: right ?
speaker: B
text: So I think they 're <disfmarker> they 're doing something like that .
speaker: B
text: I think that they 're <disfmarker> they 're <disfmarker>
speaker: B
text: I think what I mean by tha that is they 're trying to come up with a block diagram that 's plausible for the standard .
speaker: B
text: In other words , it 's <disfmarker> uh <disfmarker>
speaker: B
text: I mean from the point of view of <disfmarker> of uh reducing the number of bits you have to transmit it 's not a bad idea to detect silence anyway .
speaker: A
text: Yeah . Yeah .
speaker: B
text: Um .
speaker: A
text: I 'm just wondering what exactly did they do up in this table if it wasn't this .
speaker: B
text: But it 's <disfmarker> the thing is it 's that <disfmarker> that <disfmarker> that 's <disfmarker> that 's I <disfmarker> I <disfmarker>
speaker: B
text: Certainly it would be tricky about it intrans in transmitting voice , <pause> uh uh for listening to , is that these kinds of things <pause> uh cut <pause> speech off a lot .
speaker: B
text: Right ?
speaker: A
text: Mm - hmm .
speaker: B
text: And so <pause> um
speaker: A
text: Plus it 's gonna introduce delays .
speaker: B
text: It does introduce delays
speaker: B
text: but they 're claiming that it 's <disfmarker> it 's within the <disfmarker> <pause> the boundaries of it .
speaker: A
text: Mmm .
speaker: B
text: And the LDA introduces delays ,
speaker: B
text: and b <pause> what he 's suggesting this here is a parallel path
speaker: B
text: so that it doesn't introduce <pause> uh , any more delay .
speaker: B
text: I it introduces two hundred milliseconds of delay
speaker: B
text: but at the same <pause> time the LDA <pause> down here <disfmarker>
speaker: B
text: I don't know <disfmarker> Wh
speaker: B
text: what 's the difference between TLDA and SLDA ?
speaker: C
text: Temporal and spectral .
speaker: B
text: Ah , thank you .
speaker: E
text: Temporal LDA .
speaker: B
text: Yeah , you would know that .
speaker: C
text: Yeah
speaker: B
text: So um . The temporal LDA does in fact include the same <disfmarker>
speaker: B
text: so that <disfmarker> I think he <disfmarker> well , by <disfmarker> by saying this is a b a tentative block di diagram I think means <pause> if you construct it this way , this <disfmarker> this delay would work in that way
speaker: B
text: and then it 'd be OK .
speaker: A
text: Ah .
speaker: B
text: They <disfmarker> they clearly did actually remove <pause> silent sections
speaker: B
text: in order <disfmarker> because they <pause> got these <pause> word error rate <pause> results .
speaker: B
text: So um I think that it 's <disfmarker> it 's nice to do that in this
speaker: B
text: because in fact , it 's gonna give a better word error result
speaker: B
text: and therefore will help within an evaluation .
speaker: B
text: Whereas to whether this would actually be in a final standard , I don't know .
speaker: B
text: Um . Uh , as you know , part of the problem with evaluation right now is that the <pause> word models are pretty bad
speaker: B
text: and nobody wants <disfmarker> <pause> has <disfmarker> has approached improving them .
speaker: B
text: So <pause> it 's possible that a lot of the problems <pause> with so many insertions and so forth would go away if they were better word models <pause> to begin with .
speaker: B
text: So <pause> this might just be a temporary thing .
speaker: B
text: But <disfmarker> But , on the other hand , and maybe <disfmarker> maybe it 's a decent idea .
speaker: B
text: So um The question we 're gonna wanna go <pause> through next week when Hynek shows up I guess is given that we 've been <disfmarker>
speaker: B
text: if you look at what we 've been trying ,
speaker: B
text: we 're uh looking at <pause> uh ,
speaker: B
text: by then I guess , combinations of features and multi - band
speaker: B
text: Uh , and we 've been looking at <pause> cross - language , cross <pause> task <pause> issues .
speaker: B
text: And they 've been not so much looking at <pause> the cross task uh multiple language issues .
speaker: B
text: But they 've been looking at uh <disfmarker> <pause> at these issues .
speaker: B
text: At the on - line normalization and the uh <pause> voice activity detection .
speaker: B
text: And I guess when he comes here we 're gonna have to start deciding about <pause> um what do we choose <pause> from what we 've looked at <pause> to um blend with <pause> some group of things in what they 've looked at
speaker: B
text: And once we choose that , <pause> how do we split up the <pause> effort ?
speaker: B
text: Uh , because we still have <disfmarker> even once we choose , <pause> we 've still got <pause> uh another <pause> month or so ,
speaker: B
text: I mean there 's holidays in the way , but <disfmarker> but uh <pause> I think the evaluation data comes January thirty - first
speaker: B
text: so there 's still a fair amount of time <pause> to do things together
speaker: B
text: it 's just that they probably should be somewhat more coherent between the two sites <pause> in that <disfmarker> that amount of time .
speaker: A
text: When they removed the silence frames , did they insert some kind of a marker so that the recognizer knows it 's <disfmarker> <pause> knows when it 's time to back trace or something ?
speaker: B
text: Well , see they ,
speaker: B
text: I <disfmarker> I think they 're
speaker: B
text: Um . I don't know the <disfmarker> <pause> the specifics of how they 're doing it .
speaker: B
text: They 're <disfmarker> <pause> they 're getting around the way the recognizer works because they 're not allowed to <pause> um , change the scripts <pause> for the recognizer , <pause> I believe .
speaker: A
text: Oh , right .
speaker: B
text: So .
speaker: A
text: Maybe they 're just inserting some nummy frames or something ?
speaker: B
text: Uh . Uh , you know that 's what I had thought .
speaker: B
text: But I don't <disfmarker> I don't think they are .
speaker: A
text: Hmm .
speaker: B
text: I mean that 's <disfmarker> sort of what <disfmarker> the way I had imagined would happen is that on the other side ,
speaker: B
text: yeah
speaker: B
text: you p put some low level noise or something .
speaker: B
text: Probably don't want all zeros .
speaker: B
text: Most recognizers don't like zeros
speaker: A
text: Hmm .
speaker: B
text: but <vocalsound> but <pause> you know , <pause> put some epsilon in or some rand
speaker: A
text: Yeah .
speaker: B
text: sorry
speaker: B
text: epsilon random variable <pause> in or something .
speaker: A
text: Some constant vector .
speaker: A
text: I mean
speaker: B
text: Maybe not a constant but it doesn't , uh <disfmarker> don't like to divide by the variance of that ,
speaker: A
text: i w Or something <disfmarker>
speaker: B
text: but I mean it 's
speaker: A
text: That 's right .
speaker: A
text: But something that <disfmarker>
speaker: A
text: what I mean is something that is <pause> very distinguishable from <pause> speech .
speaker: B
text: Mm - hmm .
speaker: A
text: So that the <disfmarker> the silence model in HTK will always pick it up .
speaker: B
text: Yeah .
speaker: B
text: So I <disfmarker> I <disfmarker> that 's what I thought they would do .
speaker: B
text: or else , uh <pause> uh maybe there is some indicator to tell it to start and stop ,
speaker: B
text: I don't know .
speaker: A
text: Hmm .
speaker: B
text: But whatever they did , I mean they have to play within the rules of this specific evaluation .
speaker: A
text: Yeah .
speaker: B
text: We c we can find out .
speaker: A
text: Cuz you gotta do something . Otherwise , if it 's just a bunch of speech , stuck together <disfmarker>
speaker: B
text: No they 're <disfmarker>
speaker: A
text: Yeah .
speaker: B
text: It would do badly
speaker: B
text: and it didn't so badly ,
speaker: A
text: Yeah , right .
speaker: B
text: right ?
speaker: B
text: So they did something .
speaker: A
text: Yeah , yeah .
speaker: B
text: Yeah .
speaker: B
text: Uh . So , OK ,
speaker: B
text: So I think <pause> this brings me up to date a bit .
speaker: B
text: It hopefully brings other <pause> people up to date a bit .
speaker: B
text: And um Um <pause> I think <disfmarker> Uh , I wanna look at these numbers off - line a little bit
speaker: B
text: and think about it and <disfmarker> <pause> and talk with everybody uh , <pause> outside of this meeting .
speaker: B
text: Um , but uh
speaker: B
text: No I mean it sounds like <disfmarker> I mean <pause> there <disfmarker> there <disfmarker> there are the usual number of <disfmarker> of <pause> little <disfmarker> little problems and bugs and so forth
speaker: B
text: but it sounds like they 're getting ironed out .
speaker: B
text: And now we 're <pause> seem to be kind of in a position to actually <pause> uh , <pause> look at stuff and <disfmarker> and <disfmarker> and compare things .
speaker: B
text: So I think that 's <disfmarker> that 's pretty good .
speaker: B
text: Um <pause> I don't know what the <disfmarker>
speaker: B
text: One of the things I wonder about , <pause> coming back to the first results you talked about , is <disfmarker> is <pause> how much , <pause> uh <pause> things could be helped <pause> by more parameters .
speaker: B
text: And uh <disfmarker> <pause> And uh how many more parameters we can afford to have , <vocalsound> <pause> in terms of the uh computational limits .
speaker: B
text: Because anyway when we go to <pause> twice as much data <pause> and have the same number of parameters ,
speaker: B
text: particularly when it 's twice as much data and it 's quite diverse ,
speaker: B
text: um , I wonder if having twice as many parameters would help .
speaker: D
text: Mm - hmm .
speaker: B
text: Uh , just have a bigger hidden layer .
speaker: B
text: Uh But <disfmarker>
speaker: B
text: I doubt it would <pause> help by forty per cent .
speaker: B
text: But <vocalsound> <pause> but uh
speaker: D
text: Yeah .
speaker: B
text: Just curious .
speaker: B
text: How are we doing on the <pause> resources ?
speaker: B
text: Disk , and <disfmarker>
speaker: D
text: I think we 're alright ,
speaker: D
text: um , <pause> not much problems with that .
speaker: B
text: OK .
speaker: B
text: Computation ?
speaker: D
text: It 's OK .
speaker: D
text: Well this table took uh <pause> more than five days to get back .
speaker: B
text: We <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: Yeah , well .
speaker: D
text: But <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: Are <disfmarker> were you folks using Gin ?
speaker: B
text: That 's a <disfmarker> that just died ,
speaker: B
text: you know ?
speaker: D
text: Mmm , no .
speaker: D
text: You were using Gin <comment> perhaps ,
speaker: D
text: yeah ?
speaker: D
text: No .
speaker: E
text: No .
speaker: B
text: No ?
speaker: B
text: Oh , that 's good .
speaker: F
text: It just died .
speaker: B
text: OK .
speaker: B
text: Yeah , <pause> we 're gonna get a replacement <pause> server that 'll be a faster server , <pause> actually .
speaker: E
text: Yes .
speaker: D
text: Hmm . <comment> Mm - hmm .
speaker: B
text: That 'll be <disfmarker> It 's a <pause> seven hundred fifty megahertz uh SUN
speaker: C
text: Tonic .
speaker: B
text: uh <pause> But it won't be installed for <pause> a little while .
speaker: B
text: U Go ahead .
speaker: G
text: Do we <disfmarker> Do we have that big new IBM machine
speaker: G
text: the , I think in th
speaker: B
text: We have the <pause> little tiny IBM machine <vocalsound> <pause> that might someday grow up to be a big <pause> IBM machine .
speaker: B
text: It 's got s slots for eight ,
speaker: B
text: uh IBM was donating five ,
speaker: B
text: I think we only got two so far ,
speaker: B
text: processors .
speaker: B
text: We had originally hoped we were getting eight hundred megahertz processors .
speaker: B
text: They ended up being five fifty .
speaker: B
text: So instead of having eight processors that were eight hundred megahertz , we ended up with two <pause> that are five hundred and fifty megahertz .
speaker: B
text: And more are supposed to come soon
speaker: B
text: and there 's only a moderate amount of dat of memory .
speaker: B
text: So I don't think <pause> anybody has been sufficiently excited by it to <pause> spend much time <pause> uh <pause> with it ,
speaker: B
text: but uh <vocalsound> Hopefully , <pause> they 'll get us some more <pause> parts , soon
speaker: B
text: and <disfmarker>
speaker: B
text: Uh , yeah , I think that 'll be <disfmarker>
speaker: B
text: once we get it populated , <pause> that 'll be a nice machine .
speaker: B
text: I mean we will ultimately get eight processors in there .
speaker: B
text: And uh <disfmarker> and uh a nice amount of memory .
speaker: B
text: Uh so it 'll be a pr pretty fast Linux machine .
speaker: G
text: And if we can do things on Linux , <pause> some of the machines we have going already , like Swede ?
speaker: B
text: Mm - hmm .
speaker: G
text: Um It seems pretty fast .
speaker: B
text: Mm - hmm .
speaker: G
text: But <disfmarker> I think Fudge is pretty fast too .
speaker: B
text: Yeah , I mean you can check with uh <pause> Dave Johnson .
speaker: B
text: I mean , it <disfmarker> it 's <disfmarker> <pause> I think the machine is just sitting there .
speaker: B
text: And it does have two processors ,
speaker: B
text: you know
speaker: B
text: and <disfmarker> <pause> Somebody could do <disfmarker> <pause> you know , uh , check out <pause> uh the multi - threading <pause> libraries .
speaker: B
text: And <pause> I mean i it 's possible that the <disfmarker>
speaker: B
text: I mean , I guess the prudent thing to do would be for somebody to do the work on <disfmarker> <pause> on getting our code running <pause> on that machine with two processors <pause> even though there aren't five or eight .
speaker: B
text: There 's <disfmarker> there 's <disfmarker> there 's gonna be debugging hassles
speaker: B
text: and then we 'd be set for when we did have five or eight , to have it really be useful .
speaker: B
text: But . <pause> Notice how I said somebody
speaker: B
text: and <vocalsound> turned my head your direction .
speaker: B
text: That 's one thing you don't get in these recordings .
speaker: B
text: You don't get the <disfmarker> <pause> don't get the visuals
speaker: B
text: but <disfmarker>
speaker: G
text: I is it um <pause> mostly um the neural network trainings that are <pause> um slowing us down or the HTK runs that are slowing us down ?
speaker: B
text: Uh , I think yes .
speaker: B
text: Uh , <vocalsound> Isn't that right ?
speaker: B
text: I mean I think you 're <disfmarker> you 're sort of held up by both ,
speaker: B
text: right ?
speaker: B
text: If the <disfmarker> if the neural net trainings were a hundred times faster <pause> you still wouldn't <pause> be anything <disfmarker>
speaker: B
text: running through these a hundred times faster
speaker: B
text: because you 'd <pause> be stuck by the HTK trainings ,
speaker: D
text: Mmm .
speaker: D
text: Yeah .
speaker: B
text: right ?
speaker: B
text: But if the HTK <disfmarker>
speaker: B
text: I mean I think they 're both <disfmarker> It sounded like they were roughly equal ?
speaker: B
text: Is that about right ?
speaker: D
text: Yeah .
speaker: B
text: Yeah .
speaker: G
text: Because , um <pause> I think that 'll be running Linux ,
speaker: G
text: and Sw - Swede and Fudge are already running Linux
speaker: G
text: so , <pause> um I could try to get <pause> um the train the neural network trainings or the HTK stuff running under Linux , and to start with I 'm <pause> wondering which one I should pick first .
speaker: B
text: Uh , probably the neural net
speaker: B
text: cuz it 's probably <disfmarker> it <disfmarker> it 's <disfmarker> <pause> it 's um <disfmarker>
speaker: B
text: Well , I <disfmarker> I don't know .
speaker: B
text: They both <disfmarker>
speaker: B
text: HTK we use for <pause> um <pause> this Aurora stuff
speaker: B
text: Um <pause> Um , I think <pause> It 's not clear yet what we 're gonna use <pause> for trainings
speaker: B
text: uh <disfmarker>
speaker: B
text: Well , <pause> there 's the trainings
speaker: B
text: uh <disfmarker> is it the training that takes the time , or the decoding ?
speaker: B
text: Uh , is it about equal <pause> between the two ?
speaker: B
text: For <disfmarker> for Aurora ?
speaker: D
text: For HTK ?
speaker: B
text: For <disfmarker>
speaker: B
text: Yeah . For the Aurora ?
speaker: D
text: Uh Training is longer .
speaker: B
text: OK .
speaker: D
text: Yeah .
speaker: B
text: OK .
speaker: B
text: Well , I don't know how we can <disfmarker>
speaker: B
text: I don't know how to <disfmarker>
speaker: B
text: Do we have HTK source ?
speaker: B
text: Is that <disfmarker>
speaker: D
text: Mmm .
speaker: B
text: Yeah .
speaker: B
text: You would think that would fairly trivially <disfmarker>
speaker: B
text: the training would , anyway ,
speaker: B
text: th the testing <pause> uh I don't <disfmarker> I don't <pause> think would <pause> parallelize all that well .
speaker: B
text: But I think <pause> that <pause> you could <pause> certainly do d um , <pause> distributed , sort of <disfmarker> <pause> Ah ,
speaker: B
text: no , it 's the <disfmarker> <pause> each individual <pause> sentence
speaker: B
text: is pretty tricky to parallelize .
speaker: B
text: But you could split up the sentences in a test set .
speaker: A
text: They have a <disfmarker> they have a thing for doing that
speaker: A
text: and th they have for awhile , in H T
speaker: B
text: Yeah ?
speaker: A
text: And you can parallelize the training .
speaker: A
text: And run it on several machines
speaker: B
text: Aha !
speaker: A
text: and it just basically keeps counts .
speaker: A
text: And there 's something <disfmarker> <pause> a final <pause> thing that you run
speaker: A
text: and it accumulates all the counts together .
speaker: D
text: Mmm .
speaker: B
text: I see .
speaker: A
text: I don't what their scripts are <pause> set up to do for the Aurora stuff ,
speaker: A
text: but <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: Something that we haven't really settled on yet is other than <pause> this Aurora stuff , <pause> uh what do we do , large vocabulary <pause> training slash testing <pause> for uh tandem systems .
speaker: B
text: Cuz we hadn't really done much with tandem systems for larger stuff .
speaker: B
text: Cuz we had this one collaboration with CMU and we used SPHINX .
speaker: B
text: Uh , we 're also gonna be collaborating with SRI
speaker: B
text: and we have their <disfmarker> have theirs .
speaker: B
text: Um <pause> So <pause> I don't know
speaker: B
text: Um . So I <disfmarker> I think the <disfmarker> the advantage of going with the neural net thing is that we 're gonna use the neural net trainings , no matter what ,
speaker: G
text: OK .
speaker: B
text: for a lot of the things we 're doing ,
speaker: B
text: whereas , w exactly which HMM <disfmarker> Gaussian - mixture - based HMM thing we use is gonna depend uh
speaker: B
text: So with that , maybe we should uh <vocalsound> go to our <nonvocalsound> digit recitation task .
speaker: B
text: And , it 's about eleven fifty .
speaker: B
text: Canned .
speaker: B
text: Uh , I can <disfmarker> I can start over here .
speaker: B
text: Great ,
speaker: B
text: uh , could you give Adam a call .
speaker: B
text: Tell him to
speaker: F
text: Oh .
speaker: B
text: He 's at two nine seven seven .
speaker: B
text: OK .
speaker: B
text: I think we can <vocalsound> @ @ You know Herve 's coming tomorrow ,
speaker: B
text: right ?
speaker: B
text: Herve will be giving a talk , yeah , talk at eleven .
speaker: B
text: Did uh , did everybody sign these consent Er everybody Has everyone signed a consent form before , on previous meetings ?
speaker: B
text: You don't have to do it again each time
speaker: B
text: Yes .
speaker: B
text: microphones off

Meeting summary:
text: The meeting was dominated by a discussion of the first results coming
in.
text: There have been four types of test, in which the training data
varies, and a variety of input features have been tried.
text: The process
and results were explained to the group, the implications of the
results discussed, and plans for moving forward were made.
text: There was also discussion of some of the work being conducted by
research partners OGI, including how the two groups should best work
together.
text: The group also briefly touched upon resource issues.
text: Speaker mn007 would like to investigate increasing the context of the
phonemes.
text: speaker mn013 does agree with mn007s assessment of the
outcome, and points out the lack of data, but acknowledges that if
mn007 is interested he will go ahead with it.
text: Must be careful in choosing which experiments to perform as an
important visitor is coming soon.
text: Also need to come up with a
stronger plan for collaboration with OGI.
text: Must decide what from both
can be brought together, and how then can the work be divided.
text: Someone (implied with gestures in the meeting) must speak to a person
outside the group with regards to using a multiprocessor Linux machine
that is available.
text: Debugging the process while there are just two
processors bodes well for when they have 8 to multi-thread.
text: Speaker mn026 volunteers to get some training running under Linux.
text: It
is agreed that he should start with the neural net training, then work
on HTK.
text: Incorrect assumptions were made when considering the on-line
normalization for the main task.  members used different values to a
previous study, and whilst it was believed not to make a difference,
it does, so networks are being retrained.
text: Currently working with noise conditions being the same in training and
test data, but there is nothing which matches the noise on the Italian
test data.
text: In fact no other language matches the noise from Aurora
data.
text: Spanish was being used to train for Italian as it was assumed they
were the most similar, but that may not be as close a match as
thought.
text: OGI have an interesting approach to Voice Activation Detection for
removing blocks of silence, that shows good results, but currently the
word model being used is too poor to make good use of this and no one
is working on improving it.
text: Speakers mn007 and fn002 have been running experiments.
text: Looking at
different features, under different training conditions.
text: Moving from
training with task data to broad data increases error rate by 10%, and
moving to multiple languages increases a further 20-30%.
text: PLP with
JRASTA better than just PLP on mismatched conditions, but slightly
worse on well matched.
text: Speaker fn002 is also looking at the HTK training, but does not yet
have results.
text: Speaker mn007 is going to start work on creating broad phonetic
categories based on various features, and combine this with original
features like PLP.
text: As yet unsure how to combine the data however.