E: Yeah .
A: Mm - hmm .
B: Though the the work which uh everybody is looking at now aims at s trying to find out what to do with these vectors , so that a g simple Gaussian classifier would be happier with it .
C: Hmm .
E: Mm - hmm .
B: or to what extent a Gaussian classifier should be unhappy uh
B: that , and how to Gaussian - ize the vectors ,
B: and E: Hmm .
B: So this is uh what 's happening .
B: Then Sunil is uh uh uh asked me f for one month 's vacation
B: and since he did not take any vacation for two years , I had no I didn't have heart to tell him no .
B: So he 's in India .
E: Wow .
B: And uh E: Is he getting married or something ?
B: Uh well , he may be looking for a girl , for for I don't I don't I don't ask .
B: I know that Naran - when last time Narayanan did that he came back engaged .
E: Right .
E: Well , I mean , I 've known other friends who they they go to Ind - they go back home to India for a month , they come back married ,
B: Yeah .
B: I know .
B: I know , I know ,
E: you know , huh .
B: and then of course then what happened with Narayanan was that he start pushing me that he needs to get a PHD because they wouldn't give him his wife .
B: And she 's very pretty and he loves her
B: and so so we had to really E: So he finally had some incentive to finish ,
B: Oh yeah .
E: huh ?
B: We had well I had a incentive because he he always had this plan except he never told me .
E: Oh .
B: Sort of figured that That was a uh that he uh he told me the day when we did very well at our NIST evaluations of speaker recognition , the technology , and he was involved there .
B: We were after presentation we were driving home and he told me .
E: When he knew you were happy ,
E: huh ?
B: Yeah .
B: So I I said " well , yeah , OK " so he took another another three quarter of the year but uh he was out .
B: So I wouldn't surprise me if he has a plan like that , though though uh Pratibha still needs to get out first .
E: Hmm .
B: Cuz Pratibha is there a a year earlier .
E: Hmm .
B: And S and Satya needs to get out very first because he 's he already has uh four years served ,
B: though one year he was getting masters .
B: So .
B: So .
C: Hmm .
E: So have the um E: when is the next uh evaluation ?
E: June or something ?
B: Which ?
B: Speaker recognition ?
E: No , for uh Aurora ?
B: Uh there , we don't know about evaluation ,
B: next meeting is in June .
E: Hmm .
B: And uh uh but like getting get together .
E: Oh , OK .
E: Are people supposed to rerun their systems ,
B: Nobody said that yet .
E: or ?
B: I assume so .
E: Hmm .
B: Uh yes , uh , but nobody even set up yet the date for uh delivering uh endpointed data .
E: Wow .
B: And this uh that that sort of stuff .
B: But I uh ,
B: yeah ,
B: what I think would be of course extremely useful , if we can come to our next meeting and say " well you know we did get fifty percent improvement .
B: If if you are interested we eventually can tell you how " , but uh we can get fifty percent improvement .
E: Mm - hmm .
B: Because people will s will be saying it 's impossible .
E: Hmm .
E: Do you know what the new baseline is ?
E: Oh , I guess if you don't have B: Twenty - two t twenty twenty - two percent better than the old baseline .
E: Using your uh voice activity detector ?
B: u Yes .
B: Yes .
B: But I assume that it will be similar , I don't I I don't see the reason why it shouldn't be .
E: Similar , yeah .
E: Mm - hmm .
B: I d I don't see reason why it should be worse .
E: Yeah .
B: Cuz if it is worse , then we will raise the objection ,
B: we say " well you know how come ? "
B: Because eh if we just use our voice activity detector , which we don't claim even that it 's wonderful , it 's just like one of them .
C: Mm - hmm .
E: Yeah .
B: We get this sort of improvement ,
B: how come that we don't see it on on on on your endpointed data ?
C: Yeah .
C: I guess it could be even better ,
C: because the voice activity detector that I choosed is something that cheating , it 's using the alignment of the speech recognition system ,
B: I think so .
B: Yeah .
B: C yeah uh
C: and only the alignment on the clean channel , and then mapped this alignment to the noisy channel .
B: and on clean speech data .
E: Oh , OK .
B: Yeah .
B: Well David told me B: David told me yesterday or Harry actually he told Harry from QualComm
B: and Harry uh brought up the suggestion we should still go for fifty percent
B: he says are you aware that your system does only thirty percent uh comparing to to endpointed baselines ?
C: Yeah .
B: So they must have run already something .
E: Hmm .
B: So .
B: And Harry said " Yeah .
B: But I mean we think that we we didn't say the last word yet , that we have other other things which we can try . "
E: Hmm .
B: So . So there 's a lot of discussion now about this uh new criterion .
C: Mm - hmm .
B: Because Nokia was objecting , with uh QualComm 's we basically supported that , we said " yes " .
C: Mm - hmm .
B: Now everybody else is saying " well you guys might must be out of your mind . "
B: uh The Guenter Hirsch who d doesn't speak for Ericsson anymore because he is not with Ericsson
B: and Ericsson may not may withdraw from the whole Aurora activity because they have so many troubles now .
E: Wow .
B: Ericsson 's laying off twenty percent of people .
A: Wow .
E: Where 's uh Guenter going ?
B: Well Guenter is already he got the job uh already was working on it for past two years or three years E: Mm - hmm .
B: he got a job uh at some some Fachschule , the technical college not too far from Aachen .
E: Hmm !
B: So it 's like professor u university professor
E: Mm - hmm .
B: you know , not quite a university , not quite a sort of it 's not Aachen University , but it 's a good school and he he 's happy .
E: Mm - hmm .
E: Hmm !
B: And he well , he was hoping to work uh with Ericsson like on t uh like consulting basis ,
E: Mm - hmm .
B: but right now he says says it doesn't look like that anybody is even thinking about speech recognition .
E: Wow !
B: They think about survival .
B: Yeah .
E: Hmm .
B: So .
B: So . But this is being now discussed right now ,
B: and it 's possible that uh that that it may get through , that we will still stick to fifty percent .
C: Mm - hmm .
B: But that means that nobody will probably get this im this improvement .
B: yet , wi with the current system .
B: Which event es essentially I think that we should be happy with
B: because that that would mean that at least people may be forced to look into alternative solutions
C: Mm - hmm .
C: Mm - hmm .
B: and C: But maybe I I mean we are not too far from from fifty percent , from the new baseline .
B: Uh , but not C: Which would mean like sixty percent over the current baseline , which is B: Yeah .
B: Yes . Yes .
B: We we getting we getting there , right .
C: Well .
C: We are around fifty , fifty - five .
B: Yeah .
C: So .
B: Yeah .
C: Mm - hmm .
B: Is it like sort of is B: How did you come up with this number ?
B: If you improve twenty by twenty percent the c the f the all baselines , it 's just a quick c comp co computation ?
C: Yeah .
C: I don't know exactly if it 's B: Uh - huh .
B: I think it 's about right .
C: Yeah , because it de it depends on the weightings
C: and B: Yeah , yeah .
C: Yeah .
C: But .
C: Mm - hmm .
E: Hmm .
E: How 's your documentation or whatever
E: it w what was it you guys were working on last week ?
C: Yeah ,
C: finally we we 've not finished with this .
C: We stopped .
D: More or less it 's finished .
C: Yeah .
D: Ma - nec to need a little more time to improve the English , and maybe s to fill in something some small detail , something like that ,
C: Mm - hmm .
E: Hmm .
D: but it 's more or less ready .
C: Yeah .
C: Well , we have a document that explain a big part of the experiments ,
D: Necessary to to include the bi the bibliography .
C: but
D: Mm - hmm .
C: it 's not , yeah , finished yet .
C: Mm - hmm .
E: So have you been running some new experiments ?
E: I I thought I saw some jobs of yours running on some of the machine C: Yeah .
C: Right .
C: We 've fff <comment> done some strange things like removing C - zero or C - one from the   the vector of parameters ,
C: and we noticed that C - one is almost not useful at all .
C: You can remove it from the vector , it doesn't hurt .
E: Really ? !
E: That has no effect ?
C: Um .
E: Eh Is this in the baseline ?
E: or in uh C: In the C: No ,
C: in the proposal .
E: in E: uh - huh , uh - huh .
B: So we were just discussing , since you mentioned that , in it w
C: Mm - hmm .
B: driving in the car with Morgan this morning , we were discussing a good experiment for b for beginning graduate student who wants to run a lot of who wants to get a lot of numbers on something
C: Mm - hmm .
B: which is , like , " imagine that you will you will start putting every co any coefficient , which you are using in your vector , in some general power .
E: In some what ?
B: General pow power .
B: Like sort of you take a s power of two , or take a square root , or something .
E: Mm - hmm .
C: Mm - hmm .
E: Mm - hmm .
B: So suppose that you are working with a s C - zer C - one .
B: So if you put it in a s square root , that effectively makes your model half as efficient .
B: Because uh your uh Gaussian mixture model ,
B: right ?
B: computes the mean .
E: Mm - hmm .
B: And and uh i i i
B: but it 's the mean is an exponent of the whatever , the the this Gaussian function .
E: You 're compressing the range ,
E: right ?
E: of that B: So you 're compressing the range of this coefficient , so it 's becoming less efficient .
B: Right ?
E: Mm - hmm .
B: So .
B: So .
B: Morgan was @ @ and he was he was saying well this might be the alternative way how to play with a with a fudge factor ,
B: you know ,
B: uh in the E: Oh .
B: you know , just compress the whole vector .
E: Yeah .
B: And I said " well in that case why don't we just start compressing individual elements , like when when B: because in old days we were doing when when people still were doing template matching and Euclidean distances , we were doing this liftering of parameters ,
B: right ?
E: Uh - huh .
B: because we observed that uh higher parameters were more important than lower for recognition .
B: And basically the the C - ze C - one contributes mainly slope ,
E: Right .
B: and it 's highly affected by uh frequency response of the of the recording equipment and that sort of thing ,
C: Mm - hmm .
E: Mm - hmm .
B: so B: so we were coming with all these f various lifters .
E: Mm - hmm .
B: uh Bell Labs had he this uh uh r raised cosine lifter which still I think is built into H HTK for reasons n unknown to anybody ,
B: but but uh we had exponential lifter , or triangle lifter , basic number of lifters .
E: Hmm .
B: And . But so they may be a way to to fiddle with the f with the f
E: Insertions .
B: Insertions , deletions , or the the giving a relative uh basically modifying relative importance of the various parameters .
C: Mm - hmm .
B: The only of course problem is that there 's an infinite number of combinations
E: Oh .
E: Uh - huh .
B: and if the if you s if y
E: You need like a some kind of a B: Yeah , you need a lot of graduate students , and a lot of computing power .
E: You need to have a genetic algorithm , that basically tries random permutations of these things .
B: I know .
B: Exactly .
B: Oh .
B: If you were at Bell Labs or I d d
B: I shouldn't be saying this in on on a mike ,
B: right ?
B: Or I uh IBM ,
B: that 's what maybe that 's what somebody would be doing .
E: Yeah .
A: Hmm .
B: Oh , I mean , I mean the places which have a lot of computing power ,
E: Mm - hmm .
B: so because it is really it 's a p it 's a it 's it will be reasonable search
E: Yeah .
B: uh
B: but I wonder if there isn't some way of doing this uh search like when we are searching say for best discriminants .
E: You know actually , I don't know that this wouldn't be all that bad .
E: I mean you you compute the features once ,
B: Yeah .
E: right ?
B: Yeah .
E: And then these exponents are just applied to that B: Absolutely .
B: And hev everything is fixed .
E: So .
B: Everything is fixed .
E: And is this something that you would adjust for training ?
B: Each each E: or only recognition ?
B: For both , you would have to do .
E: You would do it on both .
B: Yeah .
E: So you 'd actually B: You have to do bo both .
B: Because essentially you are saying " uh this feature is not important " .
E: Mm - hmm .
B: Or less important ,
B: so that 's th that 's a that 's a painful one ,
B: yeah .
E: So for each uh set of exponents that you would try , it would require a training and a recognition ?
B: Yeah .
B: But but wait a minute .
B: You may not need to re uh uh retrain the m model .
B: You just may n may need to c uh give uh less weight to to uh a mod uh a component of the model which represents this particular feature .
B: You don't have to retrain it .
E: Oh .
E: So if you E: Instead of altering the feature vectors themselves , you you modify the the the Gaussians in the models .
B: You just multiply .
B: Yeah .
B: Yep .
B: You modify the Gaussian in the model ,
B: but in the in the test data you would have to put it in the power ,
B: but in a training what you c in a training uh in trained model , all you would have to do is to multiply a model by appropriate constant .
E: Uh - huh .
E: But why if you 're if you 're multi if you 're altering the model , why w in the test data , why would you have to muck with the uh cepstral coefficients ?
B: Because in uh test in uh test data you ca don't have a model .
B: You have uh only data .
B: But in a in a tr
E: No . But you 're running your data through that same model .
B: That is true ,
B: but w I mean , so what you want to do You want to say if uh obs you if you observe something like Stephane observes , that C - one is not important , you can do two things .
E: Mm - hmm .
E: Mm - hmm .
B: If you have a trained trained recognizer , in the model , you know the the the the component which I I mean di dimension wh
E: Mm - hmm .
E: All of the all of the mean and variances that correspond to C - one , you put them to zero .
B: To the s you you know it .
E: Yeah .
B: But what I 'm proposing now , if it is important but not as important , you multiply it by point one in a model .
B: But but but E: But what are you multiplying ?
E: Cuz those are means ,
E: right ?
E: I mean you 're A: You 're multiplying the standard deviation ?
A: So it 's B: I think that you multiply the B: I would I would have to look in the in the math ,
B: I mean how how does the model uh E: I think you E: Yeah , I think you 'd have to modify the standard deviation or something , so that you make it wider or narrower .
B: Yeah .
A: Cuz B: Yeah .
A: Yeah .
C: Yeah .
B: Effectively , that 's that that 's I B: Exactly .
B: That 's what you do .
B: That 's what you do , you you you modify the standard deviation as it was trained .
A: Yeah .
B: Effectively you , you know y in f in front of the of the model , you put a constant .
B: S yeah effectively what you 're doing is you is you are modifying the the the deviation .
B: Right ?
E: Oop .
A: The spread ,
E: Sorry .
A: right .
B: Yeah ,
B: the spread .
E: So .
A: It 's the same same mean ,
B: And and and A: right ?
E: So by making th the standard deviation narrower , <comment> uh your scores get worse for B: Yeah .
E: unless it 's exactly right on the mean .
B: Your als
B: No .
B: By making it narrower ,
E: Right ?
E: I mean there 's you 're you 're allowing for less variance .
B: uh y your A: Mm - hmm .
B: Yes ,
B: so you making this particular dimension less important .
B: Because see what you are fitting is the multidimensional Gaussian ,
B: right ?
E: Mm - hmm .
B: It 's a it has it has uh thirty - nine dimensions , or thirteen dimensions if you g ignore deltas and double - deltas .
E: Mm - hmm .
E: Mm - hmm .
B: So in order if you in order to make dimension which which Stephane sees uh less important , uh uh I mean not not useful , less important , what you do is that this particular component in the model you can multiply by w you can you can basically de - weight it in the model .
B: But you can't do it in a in a test data
B: because you don't have a model for th I mean uh when the test comes ,
B: but what you can do is that you put this particular component in and and you compress it .
B: That becomes uh th gets less variance , subsequently becomes less important .
E: Couldn't you just do that to the test data and not do anything with your training data ?
B: That would be very bad ,
B: because uh your t your model was trained uh expecting uh ,
B: that wouldn't work .
B: Because your model was trained expecting a certain var variance on C - one .
E: Uh - huh .
B: And because the model thinks C - one is important .
B: After you train the model , you sort of y you could do you could do still what I was proposing initially ,
B: that during the training you you compress C - one that becomes then it becomes less important in a training .
E: Mm - hmm .
B: But if you have if you want to run e ex extensive experiment without retraining the model , you don't have to retrain the model .
B: You train it on the original vector .
B: But after , you wh when you are doing this parametric study of importance of C - one you will de - weight the C - one component in the model ,