A: and so you don't necessarily want to train everything up towards the B: So where did he get his uh his tar his uh high - level targets about what 's sonorant and what 's not ?
E: From uh canonical mappings <comment> um at first
B: OK .
A: Yeah .
E: and then it 's unclear um eh
B: Using TIMIT ?
B: or using E: using TIMIT
E: right , right .
B: Uh - huh .
A: Yeah .
E: And then uh he does some fine tuning um for um special cases .
E: Yeah .
A: Yeah .
A: I mean we ha we have a kind of iterative training because we do this embedded Viterbi ,
A: uh so there is some something that 's suggested , based on the data
A: but it 's it 's not A: I think it s doesn't seem like it 's quite the same , cuz of this A: cuz then whatever that alignment is , it 's that for all all bands .
B: Mm - hmm .
A: Well no , that 's not quite right , we did actually do them separate tried to do them separately
A: so that would be a little more like what he did .
A: Um .
A: But it 's still not quite the same because then it 's it 's um setting targets based on where you would say the sound begins in a particular band .
A: Where he 's s this is not a labeling per se .
A: Might be closer I guess if we did a soft soft target uh uh embedded neural net training like we 've done a few times
A: uh f the forward um do the forward calculations to get the gammas and train on those .
A: Mmm .
A: Uh what 's next ?
B: I could say a little bit about w stuff I 've been playing with .
A: Oh .
B: I um
A: You 're playing ?
B: Huh ?
A: You 're playing ?
B: Yes , I 'm playing .
B: Um so I wanted to do this experiment to see um uh what happens if we try to uh improve the performance of the back - end recognizer for the Aurora task
B: and see how that affects things .
B: And so I had this um I think I sent around last week a  this plan I had for an experiment ,
B: this matrix where I would take the um the original um the original system .
B: So there 's the original system trained on the mel cepstral features
B: and then com and then uh optimize the b HTK system and run that again .
B: So look at the difference there
B: and then uh do the same thing for the ICSI - OGI front - end .
A: What which test set was this ?
B: This is B: that I looked at ?
A: Mm - hmm .
B: Uh I 'm looking at the Italian right now .
A: Mm - hmm .
B: So as far as I 've gotten is I 've uh been able to go through from beginning to end the um full HTK system for the Italian data
B: and got the same results that um that uh Stephane had .
B: So um I started looking to and now I 'm I 'm sort of lookin at the point where I wanna know what should I change in the HTK back - end in order to try to uh to improve it .
B: So .
B: One of the first things I thought of was the fact that they use the same number of states for all of the models
A: Mm - hmm .
B: and so I went on - line and I uh found a pronunciation dictionary for Italian digits
A: Mm - hmm .
B: and just looked at , you know , the number of phones in each one of the digits .
B: Um you know , sort of the canonical way of setting up a an HMM system is that you use um three states per phone
B: and um so then the the total number of states for a word would just be , you know , the number of phones times three .
B: And so when I did that for the Italian digits , I got a number of states , ranging on the low end from nine to the high end , eighteen .
B: Um . Now you have to really add two to that because in HTK there 's an initial null and a final null
B: so when they use uh models that have eighteen states , there 're really sixteen states .
B: They 've got those initial and final null states .
B: And so um  their guess of eighteen states seems to be pretty well matched to the two longest words of the Italian digits ,
B: the four and five which um , according to my , you know , sort of off the cuff calculation , should have eighteen states each .
A: Mm - hmm .
B: And so they had sixteen .
B: So that 's pretty close .
B: Um  but for the most of the words are sh much shorter .
B: So the majority of them wanna have nine states .
B: And so theirs are s sort of twice as long .
B: So my guess uh
B: And then if you I I printed out a confusion matrix um uh for the well - matched case ,
B: and it turns out that the longest words are actually the ones that do the best .
B: So my guess about what 's happening is that you know , if you assume a fixed the same amount of training data for each of these digits
B: and a fixed length model for all of them
B: but the actual words for some of them are half as long
B: you really um have , you know , half as much training data for those models .
B: Because if you have a long word and you 're training it to eighteen states ,  uh you 've got you know , you 've got the same number of Gaussians , you 've gotta train in each case ,
A: Mm - hmm .
B: but for the shorter words , you know , the total number of frames is actually half as many .
A: Mm - hmm .
B: So it could be that , you know , for the short words there 's because you have so many states , you just don't have enough data to train all those Gaussians .
B: So um I 'm going to try to um create more word - specific um uh prototype H M Ms to start training from .
A: Yeah , I mean , it 's not at all uncommon you do worse on long word on short words than long words anyway just because you 're accumulating more evidence for the for the longer word ,
B: Mm - hmm .
A: but .
B: Yeah so I 'll I 'll , the next experiment I 'm gonna try is to just um you know create uh models that seem to be more w matched to my guess about how long they should be .
A: Mm - hmm .
B: And as part of that um I wanted to see sort of how the um B: how these models were coming out , you know , what w when we train up uh th you know , the model for " one " , which wants to have nine states , you know ,
B: what is the uh what do the transition probabilities look like in the self - loops , <comment> look like in in those models ?
B: And so I talked to Andreas and he explained to me how you can calculate the expected duration of an HMM just by looking at the transition matrix
A: Mm - hmm .
B: and so I wrote a little Matlab script that calculates that
B: and
B: so I 'm gonna sort of print those out for each of the words to see what 's happening , you know , how these models are training up ,
A: Mm - hmm .
B: you know , the long ones versus the short ones .
A: Mm - hmm .
B: I d I did B: quickly , I did the silence model
B: and  and um
B: that 's coming out with about one point two seconds as its average duration
B: and the silence model 's the one that 's used at the beginning and the end of each of the string of digits .
A: Wow .
A: Lots of silence .
B: Yeah , yeah .
B: And so the S P model , which is what they put in between digits , I I haven't calculated that for that one yet ,
B: but um .
B: So they basically their  their model for a whole digit string is silence digit , SP , digit , SP blah - blah - blah and then silence at the end .
B: And so .
A: Are the SP 's optional ?
A: I mean skip them ?
B: I have to look at that , but I 'm not sure that they are .
B: Now the one thing about the S P model is really it only has a single s emitting state to it .
A: Mm - hmm .
B: So if it 's not optional , you know , it 's it 's not gonna hurt a whole lot
A: I see .
B: and it 's tied to the center state of the silence model so it 's not its own B: um It doesn't require its own training data ,
A: Mm - hmm .
B: it just shares that state .
A: Mm - hmm .
B: So it , I mean , it 's pretty good the way that they have it set up ,
B: but um
B: i So I wanna play with that a little bit more .
B: I 'm curious about looking at , you know how these models have trained and looking at the expected durations of the models
B: and I wanna compare that in the the well - matched case f to the unmatched case , and see if you can get an idea of B: just from looking at the durations of these models , you know , what what 's happening .
A: Yeah , I mean , I think that uh , as much as you can , it 's good to d sort of not do anything really tricky .
B: Mm - hmm .
A: Not do anything that 's really finely tuned ,
A: but just sort of eh you know you t you i z
B: Yeah .
A: The premise is kind of you have a a good person look at this for a few weeks and what do you come up with ?
B: Mm - hmm .
B: Mm - hmm .
A: And uh
B: And Hynek , when I wa told him about this , he had an interesting point ,
B: and that was th um the the final models that they end up training up have I think probably something on the order of six Gaussians per state .
B: So they 're fairly , you know , hefty models .
B: And Hynek was saying that well , probably in a real application , you wouldn't have enough compute to handle models that are very big or complicated .
B: So in fact what we may want are simpler models .
A: Could be .
B: And compare how they perform to that .
B: But you know , it depends on what the actual application is
B: and it 's really hard to know what your limits are in terms of how many Gaussians you can have .
A: Right .
A: And that , I mean , at the moment that 's not the limitation ,
A: so .
B: Mm - hmm .
A: I mean , I I I what I thought you were gonna say i but which I was thinking was um
A: where did six come from ?
A: Probably came from the same place eighteen came from .
B: Yeah .
A: You know ,
A: so .
B: Right .
A: Uh that 's another parameter ,
A: right ?
B: Yeah , yeah .
A: that that maybe , you know , uh you really want three or nine or B: Well one thing I mean , if I if if I start um reducing the number of states for some of these shorter models that 's gonna reduce the total number of Gaussians .
B: So in a sense it 'll be a simpler system .
A: Right .
A: Yeah .
A: Yeah .
A: But I think right now again the idea is doing just very simple things
B: Yeah .
A: how much better can you make it ?
B: Mm - hmm .
A: And um since they 're only simple things there 's nothing that you 're gonna do that is going to blow up the amount of computation
B: Right .
A: um so
B: Right .
A: if you found that nine was better than six that would be O K , I think , actually .
B: Mm - hmm .
B: Yeah .
A: Doesn't have to go down .
B: I really wasn't even gonna play with that part of the system yet ,
B: I was just gonna change the the t
A: Mm - hmm , OK .
A: Yeah , just work with the models ,
B: yeah , just look at the length of the models and just see what happens .
A: yeah .
A: Yeah .
B: So .
A: Cool .
A: OK .
A: So uh what 's uh
A: I guess your plan for You you you guys ' plan for the next next week is just continue on these these same things we 've been talking about for Aurora and
C: Yeah ,
C: I guess we can try to have some kind of new baseline for next week perhaps .
C: with all these minor things  modified .
C: And then do other things ,
C: play with the spectral subtraction ,
C: and retry the MSG and things like that .
A: Yeah .
A: Yeah .
A: Yeah we we have a big list .
C: Big list ?
A: You have a big list of  of things to do .
A: So .
A: Well that 's good .
A: I think that after all of this uh um confusion settles down in another A: some point a little later next year there will be some sort of standard and it 'll get out there
A: and hopefully it 'll have some effect from something that  that has uh been done by our group of people
A: but uh
A: e even if it doesn't there 's  there 's go there 'll be standards after that .
A: So .
B: Does anybody know how to um run Matlab
B: sort of in batch mode like you c send it s a bunch of commands to run and it gives you the output .
B: Is it possible to do that ?
E: I I think uh Mike tried it
B: Yeah ?
E: and he says it 's impossible so he went to Octave .
E: Octave is the um UNIX clone of of Matlab which you can batch .
B: Octave .
B: Ah !
B: OK .
B: Great .
B: Thanks .
E: Yeah .
B: I was going crazy trying to do that .
A: Huh .
E: Yeah .
C: What is Octave so ?
E: What 's that ?
C: It 's a free software ?
E: Uh , Octave ?
C: Yeah .
E: Yeah it 's it 's it 's free .
E: I think we have it here <comment> r running somewhere .
B: Great !
E: Yeah .
C: And it does the same syntax and everything
C: eh
E: Um <comment> i it 's a little behind ,
C: like Matlab , or ?
E: it 's the same syntax but it 's a little behind in that <comment> Matlab went to these like um
E: you can have cells and you can you can <comment> uh implement object - oriented type things with Matlab .
E: Uh Octave doesn't do that yet ,
E: so I think you ,
E: Octave is kinda like Matlab
E: um four point something or .
B: If it 'll do like a lot of the basic matrix and vector stuff
E: The basic stuff , right .
B: that 's perfect .
E: Yeah .
B: Great !
A: OK , guess we 're done .
E: OK .
F: Well , although by the way .