PhD E: That could be set up fairly easily I think , and you have a whole bunch of you know Professor B: Chuck is getting himself in trouble .
PhD E: That 's an interesting idea , actually . For testing the Yeah . Huh !
Grad A: Didn't you say you got these uh HTK 's set up on the new Linux boxes ?
PhD E: That 's right .
Grad A: Yeah .
Professor B: Hey !
PhD E: In fact , and and they 're just t right now they 're installing uh increasing the memory on that uh the Linux box .
Professor B: And Chuck is sort of really fishing for how to keep his computer busy ,
Grad A: Right .
Professor B: right ?
PhD E: Yeah . Absinthe .
Professor B: Well , you know , that 's PhD E: Absinthe . We 've got five processors on that .
Grad A: Oh yeah .
Professor B: that 's yeah , that 's a good thing
Grad A: That 's right .
Professor 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 .
PhD E: And two gigs of memory .
PhD C: Yeah .
PhD E: Yeah .
Grad A: Pretend , yeah .
PhD E: Exactly . Yeah .
PhD D: Go fishing .
PhD E: See how many cycles we used ?
Professor B: Yeah . Then you are sort of in this mode like all of those ARPA people are , right ?
PhD E: Yeah .
Professor B: Uh , since it is on the record , I can't say uh which company it was , but it was reported to me that uh somebody visited a company and during a d during a discussion , there was this guy who was always hitting the carriage returns uh on a computer .
PhD E: Uh - huh .
Professor B: So after two hours uh the visitor said " wh why are you hitting this carriage return ? " And he said " well you know , we are being paid by a computer ty I mean we are we have a government contract . And they pay us by by amount of computer time we use . " It was in old days when there were uh of PDP - eights and that sort of thing .
PhD E: Oh , my gosh ! So he had to make it look like Professor 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 .
PhD E: Yeah . How Idle time .
Grad A: Yeah .
PhD E: Yeah .
Professor B: Nobody was looking even at what was coming out .
PhD E: Have you ever seen those little um 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 ?
Professor B: Yeah , I know , right .
PhD E: So if you could hook that up so it hit the keyboard Professor B: Yeah . Yeah . Yeah . Yeah .
PhD E: That 's an interesting experiment .
Professor B: It would be similar similar to I knew some people who were uh that was in old Communist uh Czechoslovakia , right ? so we were watching for American airplanes , coming to spy on on uh on us at the time ,
PhD E: Mm - hmm . Mm - hmm .
Professor B: so there were three guys uh uh stationed in the middle of the woods on one l lonely uh watching tower , pretty much spending a year and a half there because there was this service right ? And so they very quickly they made friends with local girls and local people in the village
PhD E: Ugh !
Professor B: and PhD E: Yeah .
Professor B: and so but they there was one plane flying over s always uh uh above , and so that was the only work which they had . They like four in the afternoon they had to report there was a plane from Prague to Brno Basically f flying there ,
PhD E: Yeah .
Professor B: so they f very q f first thing was that they would always run back and and at four o ' clock and and quickly make a call , " this plane is uh uh passing " then a second thing was that they they took the line from this u u post to uh uh a local pub . And they were calling from the pub . And they but third thing which they made , and when they screwed up , they finally they had to p the the p the pub owner to make these phone calls because they didn't even bother to be there anymore . And one day there was there was no plane . At least they were sort of smart enough that they looked if the plane is flying there , right ? And the pub owner says " oh my four o ' clock , OK , quickly p pick up the phone , call that there 's a plane flying . "
PhD E: Yeah .
Professor B: There was no plane for some reason ,
PhD E: And there wasn't ?
Professor B: it was downed , or  and so they got in trouble . But . But uh .
PhD E: Huh ! Well that 's that 's a really i
Professor B: So . So . Yeah .
PhD E: That wouldn't be too difficult to try .
Professor B: Yeah .
PhD E: Maybe I could set that up .
Professor B: Yeah .
PhD E: And we 'll just Professor B: Well , at least go test the s test the uh assumption about C - C - one I mean to begin with . But then of course one can then think about some predictable result to change all of them .
PhD C: Mm - hmm .
Professor B: It 's just like we used to do these uh these uh um the the uh distance measures . It might be that uh PhD E: Yeah , so the first set of uh variance weighting vectors would be just you know one modifying one and leaving the others the same .
Professor B: Yeah . Yeah . Yeah . Yeah . Yeah .
PhD C: Yeah . Maybe .
PhD E: And and do that for each one .
Professor B: Because you see , I mean , what is happening here in a in a in a in such a model is that it 's tells you yeah what has a low variance uh is uh is uh is more reliable ,
PhD E: That would be one set of experiment Professor B: right ? How do we PhD E: Wh - yeah , when the data matches that , then you get really Professor B: Yeah . Yeah . Yeah . Yeah . Yeah .
PhD E: Yeah . Right .
Professor B: How do we know , especially when it comes to noise ?
PhD E: But there could just naturally be low variance .
Professor B: Yeah ?
PhD E: Because I Like , I 've noticed in the higher cepstral coefficients , the numbers seem to get smaller , right ? So d
PhD C: They t
PhD E: I mean , just naturally .
PhD C: Yeah .
Professor B: Yeah , th that 's PhD C: They have smaller means , also . Uh .
PhD E: Yeah . Exactly . And so it seems like they 're already sort of compressed .
PhD C: Uh - huh .
PhD E: The range of values .
Professor B: Yeah that 's why uh people used these lifters were inverse variance weighting lifters basically that makes uh uh Euclidean distance more like uh Mahalanobis distance with a diagonal covariance when you knew what all the variances were over the old data .
PhD E: Mm - hmm . Mm - hmm . Hmm .
Professor B: What they would do is that they would weight each coefficient by inverse of the variance . Turns out that uh the variance decreases at least at fast , I believe , as the index of the cepstral coefficients . I think you can show that uh uh analytically .
PhD E: Mm - hmm .
Professor B: So typically what happens is that you you need to weight the uh weight the higher coefficients more than uh the lower coefficients .
PhD E: Hmm . Mm - hmm . Hmm .
Professor B: So .
PhD C: Mmm .
PhD E :