B: and you will put in the you will compress the this component in a in the test data . s by the same amount .
E: Could you also if you wanted to E: if you wanted to try an experiment uh by leaving out say , C - one , couldn't you , in your test data , uh modify the all of the C - one values to be um way outside of the normal range of the Gaussian for C - one that was trained in the model ?
E: So that effectively , the C - one never really contributes to the score ?
C: Mm - hmm .
E: Do you know what I 'm say
B: No ,
B: that would be a severe mismatch ,
B: right ?
B: what you are proposing ?
E: Yeah ,
E: someth
B: N no you don't want that .
B: Because that would then your model would be unlikely .
B: Your likelihood would be low ,
B: right ?
E: Mm - hmm .
B: Because you would be providing severe mismatch .
E: But what if you set if to the mean of the model , then ?
E: And it was a cons you set all C - ones coming in through your test data , you you change whatever value that was there to the mean that your model had .
B: No that would be very good match ,
B: right ?
E: Yeah .
B: That you would C: Which C: Well , yeah , but we have several means .
C: So .
B: I see what you are sa saying ,
C: Right ?
A: Saying .
B: but
B: uh , no , no I don't think that it would be the same .
B: I mean , no , the If you set it to a mean , that would B: No , you can't do that .
E: Oh , that 's true ,
B: Y you ca you ca Ch - Chuck , you can't do that .
E: right ,
E: yeah , because you you have C: Wait .
C: Which E: Yeah .
B: Because that would be a really f fiddling with the data ,
B: you can't do that .
E: Mm - hmm . Mm - hmm .
B: But what you can do , I 'm confident you ca
B: well , I 'm reasonably confident and I putting it on the record ,
B: right ?
B: I mean y people will listen to it for for centuries now ,
B: is what you can do , is you train the model uh with the with the original data .
A: Mm - hmm .
B: Then you decide that you want to see how important C C - one is .
B: So what you will do is that a component in the model for C - one , you will divide it by by two .
B: And you will compress your test data by square root .
E: Mm - hmm .
B: Then you will still have a perfect m match .
B: Except that this component of C - one will be half as important in a in a overall score .
E: Mm - hmm .
E: Mm - hmm .
B: Then you divide it by four and you take a square , f fourth root .
B: Then if you think that some component is more is more important then th th th it then then uh uh i it is , based on training , then you uh multiply this particular component in the model by by by E: You 're talking about the standard deviation ?
B: yeah .
E: Yeah .
B: Yeah , multiply this component uh i it by number b larger than one ,
E: Mm - hmm .
B: and you put your data in power higher than one .
B: Then it becomes more important . In the overall score , I believe .
E: But don't you have to do something to the mean , also ?
C: Yeah ,
C: but , at the B: No .
C: No .
B: No .
A: Yeah .
C: But I think it 's uh the The variance is on on the denominator in the in the Gaussian equation .
C: So . I think it 's maybe it 's the contrary .
C: If you want to decrease the importance of a c parameter , you have to increase it 's variance .
B: Yes .
B: Right .
B: Yes .
D: Multiply .
B: Exactly .
B: Yeah .
B: So you so you may want to do it other way around ,
C: Hmm .
C: That 's right .
C: OK .
B: yeah .
C: Mm - hmm .
A: Right .
E: But if your E: If your um original data for C - one had a mean of two .
B: Uh - huh .
E: And now you 're you 're you 're changing that by squaring it .
E: Now your mean of your C - one original data has <comment> is four .
E: But your model still has a mean of two .
E: So even though you 've expended the range , your mean doesn't match anymore .
C: Mm - hmm .
E: Do you see what I mean ?
B: Let 's see .
C: I think What I see What could be done is you don't change your features , which are computed once for all ,
B: Uh - huh .
C: but you just tune the model .
C: So . You have your features .
C: You train your your model on these features .
E: Mm - hmm .
C: And then if you want to decrease the importance of C - one you just take the variance of the C - one component in the in the model and increase it if you want to decrease the importance of C - one or decrease it E: Yeah .
B: Yeah .
E: Right .
B: Yeah .
B: You would have to modify the mean in the model .
B: I you I agree with you .
B: Yeah .
B: Yeah ,
E: Yeah , so y
B: but I mean , but it 's it 's i it 's do - able ,
C: Well .
B: right ?
B: I mean , it 's predictable .
B: Uh . Yeah .
E: It 's predictable , yeah .
B: Yeah .
B: Yeah , it 's predictable .
E: Yeah .
C: Mmm .
E: But as a simple thing , you could just just muck with the variance .
C: Just adjust the model ,
C: yeah .
E: to get uh this uh this the effect I think that you 're talking about ,
B: Mm - hmm .
E: right ?
B: It might be .
E: Could increase the variance to decrease the importance .
B: Mm - hmm .
C: Mm - hmm .
E: Yeah , because if you had a huge variance , you 're dividing by a large number , <comment> you get a very small contribution .
C: Yeah ,
C: it becomes more flat
A: Doesn't matter C: and A: Right .
B: Yeah .
E: Yeah .
C: Yeah .
E: Hmm .
A: Yeah , the sharper the variance , the more more important to get that one right .
B: Mm - hmm .
E: Yeah , you know actually , this reminds me of something that happened uh when I was at BBN .
E: We were playing with putting um pitch into the Mandarin recognizer .
B: Mm - hmm .
E: And this particular pitch algorithm um when it didn't think there was any voicing , was spitting out zeros .
E: So we were getting uh when we did clustering , we were getting groups uh of features
B: p Pretty new outliers , interesting outliers ,
E: yeah , with with a mean of zero and basically zero variance .
B: right ?
B: Variance .
E: So , when ener <comment> when anytime any one of those vectors came in that had a zero in it , we got a great score .
E: I mean it was just , <nonvocalsound> you know , incredibly <nonvocalsound> high score , and so that was throwing everything off .
C: Mm - hmm .
E: So if you have very small variance you get really good scores when you get something that matches .
B: Yeah .
E: So . So that 's a way , yeah , yeah C: Mm - hmm .
E: That 's a way to increase the yeah , n
E: That 's interesting .
E: So in fact , that would be E: That doesn't require any retraining .
B: Yeah .
C: No ,
B: No . No .
C: that 's right .
E: So that means it 's just
C: So it 's
C: just tuning the models and testing , actually .
B: Yeah .
E: recognitions .
E: Yeah .
B: Yeah .
E: You you have a step where you you modify the models , make a d copy of your models with whatever variance modifications you make , and rerun recognition .
C: It would be quick .
B: Yeah .
C: Mm - hmm .
B: Yeah .
B: Yeah .
B: Yeah .
E: And then do a whole bunch of those .
B: Yeah .
C: Mm - hmm .
E: That could be set up fairly easily I think ,
E: and you have a whole bunch of
E: you know B: Chuck is getting himself in trouble .
E: That 's an interesting idea , actually .
E: For testing the E: Yeah .
E: Huh !
A: Didn't you say you got these uh HTK 's set up on the new Linux boxes ?
E: That 's right .
E: In fact , and and they 're just t right now they 're installing uh increasing the memory on that uh the Linux box .
A: Yeah .
B: Hey !
B: And Chuck is sort of really fishing for how to keep his computer busy ,
A: Right .
E: Yeah .
B: right ?
E: Absinthe .
E: Absinthe .
B: Well , you know , that 's E: We 've got five processors on that .
B: that 's yeah , that 's a good thing
A: Oh yeah .
E: And two gigs of memory .
A: That 's right .
B: because then y you just write the " do " - loops and then you pretend that you are working while you are sort of you c you can go fishing .
C: Yeah .
E: Yeah .
E: Exactly .
A: Pretend , yeah .
E: Yeah .
D: Go fishing .
E: See how many cycles we used ?
B: Yeah .
B: Then you are sort of in this mode like all of those ARPA people are ,
E: Yeah .
B: right ?
B: Uh , since it is on the record , I can't say uh which company it was ,
B: but it was reported to me that uh somebody visited a company
B: and during a d during a discussion , there was this guy who was always hitting the carriage returns uh on a computer .
E: Uh - huh .
B: So after two hours uh the visitor said " wh why are you hitting this carriage return ? "
B: And he said " well you know , we are being paid by a computer ty I mean we are we have a government contract .
B: And they pay us by by amount of computer time we use . "
B: It was in old days when there were uh of PDP - eights and that sort of thing .
E: Oh , my gosh !
E: So he had to make it look like B: Because so they had a they literally had to c monitor at the time at the time on a computer how much time is being spent I i i or on on this particular project .
E: Yeah .
E: How E: Idle time .
E: Yeah .
A: Yeah .
B: Nobody was looking even at what was coming out .
E: Have you ever seen those little
E: um E: It 's it 's this thing that 's the shape of a bird and it has a red ball and its beak dips into the water ?
B: Yeah , I know ,
B: right .