I: Well I: But D: But then we had a slight difference in the in the scale .
D: Uh , so .
I: Since currently the Feacalc program doesn't allow me to change the filter shape independently of the scale .
F: Uh - huh .
I: And , I did the experiment on the SRI front - end where I tried the y where the standard used to be to use trapezoidal filters .
I: You can actually continuously vary it between the two .
I: And so I wen I swi I tried the trap eh , triangular ones .
I: And it did slightly worse , but it 's really a small difference .
F: Hmm .
I: So D: Coup - Couple tenths of a percent or something .
F: OK .
I: Yeah , exactly .
F: So it 's not just losing some frequency range .
D: Right .
I: So , it 's not I: I don't think the filter shape by itself will make a huge <comment> difference .
D: Yeah .
D: Right .
D: So the oth the other thing that F: Yeah .
D: So , f
D: i
D: We 've always viewed it , anyway , as the major difference between the two , is actually in the smoothing ,
I: Mm - hmm .
D: that the that the , um , PLP , and and the reason PLP has been advantageous in , uh , slightly noisy situations is because , PLP does the smoothing at the end by an auto - regressive model ,
I: Mm - hmm .
D: and mel cepstrum does it by just computing the lower cepstral coefficients .
I: Mm - hmm .
D: Um . So , um Mm - hmm .
I: OK .
I: So one thing I haven't done yet is to actually do all of this with a much larger with our full training set .
I: So right now , we 're using a I: I don't know ,
I: forty ?
I: I i it 's it 's eh <comment> it 's a f training set that 's about , um , you know , by a factor of four smaller than what we use when we train the full system .
I: So , some of these smoothing issues are over - fitting for that matter .
I: And the Baum - Welch should be much less of a factor , if you go full whole hog .
D: Mm - hmm .
D: Could be .
D: Yeah .
I: And so , w so , just um so the strategy is to first sort of treat things with fast turn - around on a smaller training set and then , when you 've sort of , narrowed it down , you try it on a larger training set .
D: Yeah .
I: And so , we haven't done that yet .
D: Now the other que related question , though , is is , uh , what 's the boot models for these things ?
I: Th - th the boot models are trained from scratch .
I: So we compute ,
I: um I: So , we start with a , um , alil alignment that we computed with the b sort of the best system we have .
I: And and then we train from scratch .
I: So we com we do a , you know , w um  We collect the uh , the observations from those alignments under each of the feature sets that that we train .
I: And then , from there we do ,
I: um I: There 's a lot of ,
I: actually  The way it works , you first train a phonetically - tied mixture model .
I: Um .
I: You do a total of I: First you do a context - independent PTM model .
I: Then you switch to a context I: You do two iterations of that .
I: Then you do two iterations of of of context - dependent phonetically - tied mixtures .
I: And then from that you you do the you you go to a state - clustered model ,
D: Yeah .
I: and you do four iterations of that .
I: So there 's a lot of iterations overall between your original boot models and the final models .
I: I don't think that I: Hmm .
I: We have never seen big differences .
I: Once I thought " oh , I can Now I have these much better models .
I: I 'll re - generate my initial alignments .
I: Then I 'll get much better models at the end . "
I: Made no difference whatsoever .
I: It 's I think it 's eh , i
D: Right .
I: the boot models are recur
D: Well , mis for making things better .
D: Yeah .
D: But , this for making things worse .
D: This it migh
D: Th - the thought is is is possible another possible partial cause is if the boot models used a comple used a different feature set , that I: Mm - hmm .
I: Mm - hmm .
I: But there are no boot models , in fact .
I: You you 're not booting from initial models .
I: You 're booting from initial alignments .
D: Which you got from a different feature set .
I: That 's correct .
D: So , those features look at the data differently , actually .
I: Yeah , but D: I mean , you know , they they will find boundaries a little differently , though D: You know , all th all that sort of thing is actually slightly different .
D: I 'd expect it to be a minor effect ,
I: But but but , what I 'm what I 'm saying is D: but I: So , we e w f w For a long time we had used boot alignments that had been trained with a  with the same front - end but with acoustic models that were , like , fifteen percent worse than what we use now .
D: Mm - hmm .
I: And with a dict different dictionary with a considerably different dictionary , which was much less detailed and much less well - suited .
D: Mm - hmm .
D: Yeah .
I: And so , then we switched to new boot alignments ,
I: which which now had the benefit of all these improvements that we 've made over two years in the system .
D: Right .
I: And , the result in the end was no different .
D: Right .
I: So , what I 'm saying is , the exact nature of these boot alignments is probably not a big factor in the quality of the final models .
D: Yeah , maybe not .
D: But it it I st still see it as I mean , there 's there 's a history to this , too ,
I: Yeah .
D: but I uh , I don't wanna go into ,
I: Mm - hmm .
D: but but I I I th I think it could be the things that it the data is being viewed in a certain way , uh , that a beginning is here rather than there and so forth ,
I: Yeah .
I: Right .
D: because the actual signal - processing you 're doing is slightly different .
I: Right .
D: But , it 's it 's that 's probably not it .
I: Yeah .
I: Anyway , I I I should really reserve , uh , any conclusions until we 've done it on the large training set , um , and until we 've seen the results with the with the VTL in training .
I: So .
D: Yeah .
D: At some point you also might wanna take the same thing and try it on , uh , some Broadcast News data or something else that actually has has some noisy  noisy components , so we can see if any conclusions we come to holds across different data .
I: Yeah .
I: Right .
D: Uh I: And , uh , with this , I have to leave .
H: Hmm !
D: OK .
D: So , is there something quick about Absinthe that you ?
I: With this said .
F: Uh . Just what we were talking about before , which is that I ported a Blass library to Absinthe ,
F: and then got got it working with fast - forward ,
F: and got  a speedup roughly proportional to the number of processors times the clock cycle .
I: Oh .
F: So , that 's pretty good .
I: Oh !
I: Cool .
F: Um , I 'm in the process of doing it for Quicknet ,
F: but there 's something going wrong
F: and it 's about half the speed that I was estimating it should be ,
F: and I 'm not sure why .
I: Mm - hmm .
F: But I 'll keep working on it .
F: But the what it means is that it 's likely that for net training and forward passes , we 'll Absinthe will be a good machine .
F: Especially if we get a few more processors and upgrade the processors .
I: A few more processors ?
I: How many are you shooting for ?
F: There 're five now .
F: It can hold eight .
I: Oh , OK .
D: Yeah , we 'll just go buy them , I guess .
F: And it 's also five - fifty megahertz and you can get a gigahertz .
I: Yeah .
F: So .
I: Can you mix t uh , processors of different speed ?
F: I don't think so .
F: I think we 'd have to do all I: OK .
D: Probably just throw away the old ones ,
F: Yep .
D: and D: Thank you for the box ,
D: and  I 'll just go buy their process .
I: Oh , OK .
H: Hmm !
I: Maybe we can stick them in another system .
I: I dunno .
F: We 'd have to get a almost certainly have to get a , uh , Netfinity server .
I: I see .
F: They 're pretty pretty specialized .
D: Yeah .
I: OK .
D: OK .
D: Is is Liz coming back , do you know ,
D: or ?
D: I dunno .
D: Yeah .
D: Oh , you don't .
D: OK .
D: Alright .
D: Alright .
D: See you .
D: Um .
D: Alright .
D: So D: Uh , they 're having tea out there .
D: So I guess the other thing that we were gonna talk about is is , uh , demo .
D: And , um , so , these are the demos for the uh , July , uh , meeting and , um DARPA mee
F: July what ?
F: Early July ?
F: Late July ?
D: Oh , I think it 's July fifteenth .
A: Sixteen to eighteen , I think .
D: Is that it ?
D: Yeah , sixteenth , eighteenth .
A: Roughly .
D: Yeah .
D: So , we talked about getting something together for that ,
D: but maybe , uh maybe we 'll just put that off for now , given that D: But I think maybe we should have a a sub - meeting ,
D: I think , uh , probably , uh , Adam and and , uh , Chuck and me should talk about should get together and talk about that sometime soon .
F: Over a cappuccino tomorrow ?
D: Yeah <comment> something like that .
D: Um , uh , you know , maybe maybe we 'll involve Dan Ellis at some some level as well .
F: Mm - hmm .
D: Um .
D: OK . The the tea is is going , so , uh , I suggest we do , uh uh , a unison .
F: A unison digits ?
A: OK .
D: Yeah .
F: Which is gonna be a little hard for a couple people because we have different digits forms .
D: Gets our E: Oops .
F: We have a I found a couple of old ones .
H: Hmm .
D: Oh .
D: Well , that 'll be interesting .
D: So ,
D: uh F: Have you done digits before ?
D: No .
C: I haven't done it .
F: OK .
F: So , uh , the idea is just to read each line with a short pause between lines ,
C: Alright .
F: not between F: And , uh , since we 're in a hurry , we were just gonna read everyone all at once .
F: So , if you sorta plug your ears and read C: OK .
F: So first read the transcript number , and then start reading the digits .
C: Sure .
F: OK ?
F: One , two , three .
D: OK we 're done .
F: And