B: so You have two net or three nets ?
B: Was this ?
B: How many how many nets do you have ?
D: I mean , It 's just Were we just changing the labels to retrain nets with fewer out outputs .
B: No nets .
E: Begin to work in this .
E: We are @ @ .
B: Right .
D: And then B: But but I didn't understand D: Mm - hmm .
B: Uh . the software currently just has uh a allows for I think , the one one hot output .
B: So you 're having multiple nets and combining them ,
B: or ?
B: Uh , how are you how are you coming up with B: If you say uh If you have a place characteristic and a manner characteristic , how do you D: It - It 's the single net ,
A: I think they have one output .
D: yeah .
B: Oh , it 's just one net .
E: Yeah .
F: mm - hmm
D: It 's one net with um twenty - seven outputs
D: if we have twenty - seven classes ,
B: I see .
D: yeah .
B: I see , OK .
D: So it 's Well , it 's basically a standard net with fewer classes .
B: So you 're sort of going the other way of what you were saying a bit ago
B: instead of D: Yeah ,
B: yeah .
D: but I think D: Yeah .
F: But including the features .
D: B b including the features , yeah .
E: Yeah .
D: I don't think this will work alone .
D: I think it will get worse
D: because Well , I believe the effect that of of too reducing too much the information is basically basically what happens
B: Uh - huh .
D: and B: But you think if you include that plus the other features ,
D: but D: Yeah ,
D: because there is perhaps one important thing that the net brings ,
D: and OGI show showed that , is the distinction between sp speech and silence
D: Because these nets are trained on well - controlled condition .
D: I mean the labels are obtained on clean speech , and we add noise after .
D: So this is one thing
D: And
D: But perhaps , something intermediary using also some broad classes could could bring so much more information .
D: Uh .
B: So so again then we have these broad classes
B: and well , somewhat broad .
B: I mean , it 's twenty - seven instead of sixty - four , basically .
D: Yeah .
B: And you have the original features .
B: Which are PLP , or something .
D: Yeah .
D: Mm - hmm .
B: And then uh , just to remind me , all of that goes into uh , that all of that is transformed by uh , uh , K - KL or something , or ?
D: There will probably be ,
E: Mu .
D: yeah ,
D: one single KL to transform everything
D: or  uh ,
B: Right .
E: No
E: transform the PLP
D: per
E: and only transform the other
E: I 'm not sure .
D: This is still something that
B: Well no ,
B: I think D: yeah ,
D: we don't know B: I see .
B: So there 's a question of whether you would E: Two e @ @ it 's one .
D: Yeah .
B: Right .
B: Whether you would transform together or just one .
B: Yeah .
B: Might wanna try it both ways .
B: But that 's interesting .
B: So that 's something that you 're you haven't trained yet but are preparing to train , and D: Yeah .
B: Yeah .
B: Um  Yeah ,
D: Mmm .
B: so I think Hynek will be here Monday .
B: Monday or Tuesday .
B: So
D: Uh , yeah .
B: So I think , you know , we need to choose the choose the experiments carefully ,
B: so we can get uh key  key questions answered uh before then
D: Mm - hmm .
B: and leave other ones aside even if it leaves incomplete tables  someplace ,
B: uh uh , it 's it 's really time to  time to choose .
D: Mm - hmm .
B: Um , let me pass this out , by the way .
B: Um These are B: Did did  did I interrupt you ?
E: Yeah , I have one .
B: Were there other things that you wanted to D: Uh , no . I don't think so .
D: Yeah , I have one .
G: Oh , thanks .
B: Ah ! OK . OK , we have lots of them .
E: We have one .
B: OK , so um , Something I asked B: So they 're they 're doing the the VAD
B: I guess they mean voice activity detection So again , it 's the silence B: So they 've just trained up a net which has two outputs , I believe .
B: Um I asked uh Hynek whether B: I haven't talked to Sunil B: I asked Hynek whether they compared that to just taking the nets we already had and summing up the probabilities .
D: Mm - hmm .
B: Uh . To get the speech voice activity detection , or else just using the silence , if there 's only one silence output .
B: Um And , he didn't think they had ,
B: um . But on the other hand , maybe they can get by with a smaller net
B: and maybe sometimes you don't run the other ,
B: maybe there 's a computational advantage to having a separate net , anyway .
D: Mm - hmm .
B: So um Their uh  the results look pretty good .
D: Yeah .
B: Um , I mean , not uniformly .
B: I mean , there 's a an example or two that you can find , where it made it slightly worse , but uh in in all but a couple examples .
D: Mmm .
B: Uh .
E: But they have a question of the result .
E: Um how are trained the the LDA filter ?
E: How obtained the LDA filter ?
D: Mmm .
B: I I 'm sorry .
B: I don't understand your question .
E: Yes ,
E: um the LDA filter needs some training set to obtain the filter .
E: Maybe
E: I don't know exactly how they are obtained .
B: It 's on training .
E: Training , with the training test of each E: You understand me ?
B: No .
E: Yeah ,
E: uh for example , LDA filter need a set of  a set of training to obtain the filter .
B: Yes .
E: And maybe for the Italian , for the TD TE on for Finnish , these filter are are obtained with their own training set .
B: Yes , I don't know .
B: That 's that 's so that 's a that 's a very good question ,
B: then now that it  I understand it . It 's
B: " yeah ,
B: where does the LDA come from ? "
B: In the In earlier experiments , they had taken LDA from a completely different database ,
E: Yeah .
B: right ?
E: Yeah , because maybe it the same situation that the neural network training with their own
D: Mmm .
E: set .
B: So that 's a good question .
B: Where does it come from ?
B: Yeah , I don't know .
B: Um , but uh to tell you the truth , I wasn't actually looking at the LDA so much when I I was looking at it
B: I was mostly thinking about the  the VAD .
B: And um , it ap it ap
B: Oh what does what does ASP ?
B: Oh that 's D: The features ,
D: yeah .
D: Yeah .
E: I don't understand also
B: It says " baseline ASP " .
E: what
E: is  what is the difference between ASP and uh baseline over ?
D: Yeah , I don't know .
C: ASP .
E: This is C: Oh .
B: Anybody know any C: There it is .
B: Um Cuz there 's " baseline Aurora " above it .
C: Mm - hmm .
B: And it 's This is mostly better than baseline ,
B: although in some cases it 's a little worse , in a couple cases .
C: Well , it says baseline ASP is twenty - three mill minus thirteen .
E: Yeah .
B: Yeah , it says what it is .
B: But I don't how that 's different from C: From the baseline . <comment> OK .
B: I think this was  I think this is the same point we were at when when we were up in Oregon .
E: Yeah .
D: I think  I think it 's the C - zero using C - zero instead of log energy .
E: Ah , OK , mm - hmm .
D: Yeah , it 's this .
E: yeah .
B: Oh . OK .
D: It should be that ,
D: yeah .
D: Because B: Shouldn't it be A: They s they say in here that the VAD is not used as an additional feature .
A: Does does anybody know how they 're using it ?
B: Yeah . So so what they 're doing here is , i
D: Yeah .
B: if you look down at the block diagram , um , they estimate they get a  they get an estimate of whether it 's speech or silence ,
A: But that B: and then they have a median filter of it .
A: Mm - hmm .
B: And so um , basically they 're trying to find stretches .
B: The median filter is enforcing a i it having some continuity .
A: Mm - hmm .
B: You find stretches where the combination of the frame wise VAD and the  the median filter say that there 's a stretch of silence .
B: And then it 's going through and just throwing the data away .
C: Hmm .
B: Right ?
B: So um A: So it 's it 's A: I don't understand .
A: You mean it 's throwing out frames ?
A: Before B: It 's throwing out chunks of frames , yeah .
B: There 's the the median filter is enforcing that it 's not gonna be single cases of frames , or isolated frames .
A: Yeah .
B: So it 's throwing out frames
B: and the thing is um , what I don't understand is how they 're doing this with H T
A: Yeah ,
B: This is A: that 's what I was just gonna ask .
A: How can you just throw out frames ?
B: Yeah .
B: Well , you you can ,
D: i
B: right ?
B: I mean y you you D: Yeah .
B: it stretches again .
B: For single frames I think it would be pretty hard .
A: Yeah .
B: But if you say speech starts here , speech ends there .
A: Mm - hmm .
B: Right ?
C: Huh .
D: Yeah .
D: Yeah , you can basically remove the the frames from the feature feature files .
B: Yeah .
B: Yeah , so I mean in the i i in the in the decoding , you 're saying that we 're gonna decode from here to here .
D: I t
A: Mm - hmm .
B: I think they 're they 're they 're treating it , you know , like uh B: well , it 's not isolated word ,
B: but but connected ,
B: you know , the the A: In the text they say that this this is a tentative block diagram of a possible configuration we could think of .
A: So that sort of sounds like they 're not doing that yet .
B: Well . No they they have numbers though ,
B: right ?
B: So I think they 're they 're doing something like that .
B: I think that they 're they 're B: 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 .
B: In other words , it 's uh B: I mean from the point of view of of uh reducing the number of bits you have to transmit it 's not a bad idea to detect silence anyway .
A: Yeah . Yeah .
B: Um .
A: I 'm just wondering what exactly did they do up in this table if it wasn't this .
B: But it 's the thing is it 's that that that 's that 's I I B: Certainly it would be tricky about it intrans in transmitting voice , uh uh for listening to , is that these kinds of things uh cut speech off a lot .
B: Right ?
A: Mm - hmm .
B: And so um
A: Plus it 's gonna introduce delays .
B: It does introduce delays
B: but they 're claiming that it 's it 's within the  the boundaries of it .
A: Mmm .
B: And the LDA introduces delays ,
B: and b what he 's suggesting this here is a parallel path
B: so that it doesn't introduce uh , any more delay .
B: I it introduces two hundred milliseconds of delay
B: but at the same time the LDA down here B: I don't know Wh
B: what 's the difference between TLDA and SLDA ?
C: Temporal and spectral .
B: Ah , thank you .
E: Temporal LDA .
B: Yeah , you would know that .
C: Yeah
B: So um . The temporal LDA does in fact include the same B: so that I think he well , by by saying this is a b a tentative block di diagram I think means if you construct it this way , this this delay would work in that way
B: and then it 'd be OK .
A: Ah .
B: They they clearly did actually remove silent sections
B: in order because they got these word error rate results .
B: So um I think that it 's it 's nice to do that in this
B: because in fact , it 's gonna give a better word error result
B: and therefore will help within an evaluation .
B: Whereas to whether this would actually be in a final standard , I don't know .
B: Um . Uh , as you know , part of the problem with evaluation right now is that the word models are pretty bad
B: and nobody wants  has has approached improving them .
B: So it 's possible that a lot of the problems with so many insertions and so forth would go away if they were better word models to begin with .
B: So this might just be a temporary thing .
B: But But , on the other hand , and maybe maybe it 's a decent idea .
B: So um The question we 're gonna wanna go through next week when Hynek shows up I guess is given that we 've been B: if you look at what we 've been trying ,
B: we 're uh looking at uh ,
B: by then I guess , combinations of features and multi - band
B: Uh , and we 've been looking at cross - language , cross task issues .
B: And they 've been not so much looking at the cross task uh multiple language issues .
B: But they 've been looking at uh  at these issues .
B: At the on - line normalization and the uh voice activity detection .
B: And I guess when he comes here we 're gonna have to start deciding about um what do we choose from what we 've looked at to um blend with some group of things in what they 've looked at
B: And once we choose that , how do we split up the effort ?
B: Uh , because we still have even once we choose , we 've still got uh another month or so ,
B: I mean there 's holidays in the way , but but uh I think the evaluation data comes January thirty - first
B: so there 's still a fair amount of time to do things together
B: it 's just that they probably should be somewhat more coherent between the two sites in that that amount of time .
A: When they removed the silence frames , did they insert some kind of a marker so that the recognizer knows it 's  knows when it 's time to back trace or something ?
B: Well , see they ,
B: I I think they 're
B: Um . I don't know the  the specifics of how they 're doing it .
B: They 're  they 're getting around the way the recognizer works because they 're not allowed to um , change the scripts for the recognizer , I believe .
A: Oh , right .
B: So .
A: Maybe they 're just inserting some nummy frames or something ?
B: Uh . Uh , you know that 's what I had thought .
B: But I don't I don't think they are .
A: Hmm .
B: I mean that 's sort of what the way I had imagined would happen is that on the other side ,
B: yeah
B: you p put some low level noise or something .
B: Probably don't want all zeros .
B: Most recognizers don't like zeros
A: Hmm .
B: but but you know , put some epsilon in or some rand
A: Yeah .
B: sorry
B: epsilon random variable in or something .
A: Some constant vector .
A: I mean
B: Maybe not a constant but it doesn't , uh don't like to divide by the variance of that ,
A: i w Or something B: but I mean it 's
A: That 's right .
A: But something that A: what I mean is something that is very distinguishable from speech .
B: Mm - hmm .
A: So that the the silence model in HTK will always pick it up .
B: Yeah .
B: So I I that 's what I thought they would do .
B: or else , uh uh maybe there is some indicator to tell it to start and stop ,
B: I don't know .
A: Hmm .
B: But whatever they did , I mean they have to play within the rules of this specific evaluation .
A: Yeah .
B: We c we can find out .
A: Cuz you gotta do something . Otherwise , if it 's just a bunch of speech , stuck together B: No they 're A: Yeah .
B: It would do badly
B: and it didn't so badly ,
A: Yeah , right .
B: right ?
B: So they did something .
A: Yeah , yeah .
B: Yeah .
B: Uh . So , OK ,
B: So I think this brings me up to date a bit .
B: It hopefully brings other people up to date a bit .
B: And um Um I think Uh , I wanna look at these numbers off - line a little bit
B: and think about it and  and talk with everybody uh , outside of this meeting .
B: Um , but uh
B: No I mean it sounds like I mean there there there are the usual number of of little little problems and bugs and so forth
B: but it sounds like they 're getting ironed out .
B: And now we 're seem to be kind of in a position to actually uh , look at stuff and and and compare things .
B: So I think that 's that 's pretty good .
B: Um I don't know what the B: One of the things I wonder about , coming back to the first results you talked about , is is how much , uh things could be helped by more parameters .
B: And uh  And uh how many more parameters we can afford to have ,  in terms of the uh computational limits .
B: Because anyway when we go to twice as much data and have the same number of parameters ,
B: particularly when it 's twice as much data and it 's quite diverse ,
B: um , I wonder if having twice as many parameters would help .
D: Mm - hmm .
B: Uh , just have a bigger hidden layer .
B: Uh But B: I doubt it would help by forty per cent .
B: But  but uh
D: Yeah .
B: Just curious .
B: How are we doing on the resources ?
B: Disk , and D: I think we 're alright ,
D: um , not much problems with that .
B: OK .
B: Computation ?
D: It 's OK .
D: Well this table took uh more than five days to get back .
B: We B: Yeah .
B: Yeah , well .
D: But D: Yeah .
B: Are were you folks using Gin ?
B: That 's a that just died ,
B: you know ?
D: Mmm , no .
D: You were using Gin <comment> perhaps ,
D: yeah ?
D: No .
E: No .
B: No ?
B: Oh , that 's good .
F: It just died .
B: OK .
B: Yeah , we 're gonna get a replacement server that 'll be a faster server , actually .
E: Yes .
D: Hmm . <comment> Mm - hmm .
B: That 'll be It 's a seven hundred fifty megahertz uh SUN
C: Tonic .
B: uh But it won't be installed for a little while .
B: U Go ahead .
G: Do we Do we have that big new IBM machine
G: the , I think in th
B: We have the little tiny IBM machine  that might someday grow up to be a big IBM machine .
B: It 's got s slots for eight ,
B: uh IBM was donating five ,
B: I think we only got two so far ,
B: processors .
B: We had originally hoped we were getting eight hundred megahertz processors .
B: They ended up being five fifty .
B: So instead of having eight processors that were eight hundred megahertz , we ended up with two that are five hundred and fifty megahertz .