A: Yeah .
C: I I thought was , like , three , two , two , five ,
C: but I I 'll I 'll double check .
A: Mm - hmm .
C: It was over a week ago that I did it ,
A: OK .
E: Oh .
C: so I can't remember exactly .
A: Mm - hmm .
C: But , uh B: Mm - hmm .
C: um , but it 's so much faster .
E: Hmm .
C: I it makes a big difference .
C: So we could do a lot more experiments and throw a lot more stuff in there .
F: Yeah .
B: That 's great .
C: Um .
C: Oh , the other thing that I did was , um , I compiled the HTK stuff for the Linux boxes .
C: So we have this big thing that we got from IBM ,
C: which is a five - processor machine .
C: Really fast ,
C: but it 's running Linux .
C: So , you can now run your experiments on that machine
C: and you can run five at a time
C: and it runs , uh , as fast as , you know , uh , five different machines .
A: Mm - hmm .
F: Mm - hmm .
C: So , um ,
C: I 've forgotten now what the name of that machine is
C: but I can I can send email around about it .
A: Yeah .
C: And so we 've got it C: now HTK 's compiled for both the Linux and for , um , the Sparcs .
C: Um , you have to make you have to make sure that in your dot CSHRC , um , it detects whether you 're running on the Linux or a a Sparc and points to the right executables .
C: Uh ,
C: and you may not have had that in your dot CSHRC before , if you were always just running the Sparc .
C: So ,
C: um ,
C: uh , I can I can tell you exactly what you need to do to get all of that to work .
A: Mm - hmm .
C: But it 'll it really increases what we can run on .
E: Hmm . Cool .
C: So , together with the fact that we 've got these faster Linux boxes and that it takes less time to do these , um , we should be able to crank through a lot more experiments .
A: Mm - hmm .
C: So .
E: Hmm .
C: So after I did that , then what I wanted to do <comment> was try increasing the number of mixtures ,
C: just to see , um see how how that affects performance .
A: Yeah .
C: So .
B: Yeah . In fact , you could do something like keep exactly the same procedure and then add a fifth thing onto it
C: Mm - hmm .
C: Exactly .
B: that had more .
B: Yeah .
C: Right .
C: Right .
E: So at at the middle o where the arrows are showing , that 's you 're adding one more mixture per state ,
C: Uh - huh .
E: or ?
C: Uh ,
C: let 's see ,
C: uh .
C: It goes from
C: this uh , try to go it backwards this at this point it 's two mixtures per state .
C: So this just adds one .
C: Except that , uh , actually for the silence model , it 's six mixtures per state .
B: Mm - hmm .
E: OK .
C: Uh , so it goes to two .
C: Um .
C: And I think what happens here is B: Might be between , uh , shared , uh shared variances or something ,
C: Yeah . I think that 's what it is .
B: or C: Uh ,
C: yeah . It 's , uh C: Shoot .
C: I I I can't remember now what happens at that first one .
C: Uh , I have to look it up and see .
E: Oh , OK .
C: Um ,
C: there because they start off with , uh , an initial model
C: which is just this global model ,
C: and then they split it to the individuals .
C: And so , it may be that that 's what 's happening here .
C: I I  I have to look it up and see .
C: I I don't exactly remember .
E: OK .
B: OK .
C: So . That 's it .
B: Alright .
B: So what else ?
A: Um .
A: Yeah .
A: There was a conference call this Tuesday .
A: Um .
A: I don't know yet the  what happened Tuesday ,
A: but the points that they were supposed to discuss is still , uh , things like the weights ,
A: uh B: Oh , this is a conference call for , uh , uh , Aurora participant sort of thing .
E: For A: Yeah .
B: I see .
A: Yeah .
A: Mmm .
B: Do you know who was who was since we weren't in on it , uh , do you know who was in from OGI ?
B: Was  was was Hynek involved
B: or was it Sunil
A: I have no idea .
B: or ?
B: Oh , you don't know .
A: Mmm , I just B: OK .
A: Yeah .
B: Alright .
A: Um ,
A: yeah .
A: So the points were the the weights how to weight the different error rates that are obtained from different language and and conditions .
A: Um ,
A: it 's not clear that they will keep the same kind of weighting .
A: Right now it 's a weighting on on improvement .
B: Mm - hmm .
A: Some people are arguing that it would be better to have weights on
A: uh A: well , to to combine error rates before computing improvement .
A: Uh , and the fact is that for right now for the English , they have weights A: they they combine error rates ,
A: but for the other languages they combine improvement .
A: So it 's not very consistent .
B: Mm - hmm .
A: Um A: Yeah .
A: The , um A: Yeah .
A: And so A: Well , this is a point .
A: And right now actually there is a thing also , uh , that happens with the current weight is that a very non - significant improvement on the well - matched case result in huge differences in  in the final number .
B: Mm - hmm .
C: Hmm .
A: And so , perhaps they will change the weights to A: Yeah .
C: How should that be done ?
C: I mean , it it seems like there 's a simple way A: Mm - hmm .
C: Uh , this seems like an obvious mistake or something .
C: Th - they 're B: Well , I mean , the fact that it 's inconsistent is an obvious mistake .
B: But the but , um , the other thing A: In
B: I don't know
B: I haven't thought it through ,
B: but one one would think that each It B: it 's like if you say what 's the what 's the best way to do an average ,
B: an arithmetic average or a geometric average ?
C: Mm - hmm .
B: It depends what you wanna show .
A: Mm - hmm .
B: Each each one is gonna have a different characteristic .
A: Yeah .
B: So C: Well , it seems like they should do , like , the percentage improvement or something ,
C: rather than the absolute improvement .
A: Tha - that 's what they do .
B: Well , they are doing that .
A: Yeah .
B: No , that is relative .
B: But the question is , do you average the relative improvements or do you average the error rates and take the relative improvement maybe of that ?
A: Yeah .
A: Yeah .
B: And the thing is it 's not just a pure average because there are these weightings .
C: Oh .
B: It 's a weighted average .
B: Um .
A: Yeah .
A: And so when you average the the relative improvement it tends to  to give a lot of of , um , importance to the well - matched case
A: because the baseline is already very good
A: and ,
A: um ,
C: Why don't they not look at improvements but just look at your av your scores ?
A: i it 's C: You know , figure out how to combine the scores
A: Mm - hmm .
C: with a weight or whatever ,
C: and then give you a score C: here 's your score .
C: And then they can do the same thing for the baseline system C: and here 's its score .
C: And then you can look at A: Mm - hmm .
B: Well , that 's what he 's seeing as one of the things they could do .
B: It 's just when you when you get all done , I think that they pro
A: Yeah .
B: I m I I wasn't there
B: but I think they started off this process with the notion that you should be significantly better than the previous standard .
C: Mm - hmm .
B: And , um ,
B: so they said " how much is significantly better ?
B: what do you ? "
B: And and so they said " well , you know , you should have half the errors , " or something , " that you had before " .
A: Mm - hmm .
A: Hmm .
C: Mm - hmm .
A: Yeah .
B: So it 's ,
B: uh ,
C: Hmm .
B: But it does seem like
B: i i it does seem like it 's more logical to combine them first
B: and then do the A: Combine error rates
B: Yeah .
A: and then A: Yeah .
B: Yeah .
A: Well A: But there is this this is this still this problem of weights .
A: When when you combine error rate it tends to give more importance to the difficult cases ,
B: Oh , yeah ?
A: and some people think that A: well , they have different , um , opinions about this .
A: Some people think that it 's more important to look at  to have ten percent imp relative improvement on well - matched case than to have fifty percent on the m mismatched ,