C: Yeah .
C: Yeah .
F: Um , of course that 's there and this is here and so on .
F: OK ?
C: OK .
C: Um , for the training corpus corpus , um , we have , um , the the d digits <nonvocalsound> from the various languages .
C: Um , English
C: Spanish
C: um , French
C: What else do we have ?
G: And the Finnish .
C: Finnish .
E: And Italian .
A: Where did th where did that come from ?
E: Uh , no , Italian no .
A: Digits ?
E: Italian no .
A: Oh .
C: Oh . Italian .
E: I Italian yes .
E: Italian ?
F: Italian .
A: Is that Was that distributed with Aurora ,
A: or ?
C: One L or two L 's ?
F: The newer one .
A: Where did that ?
G: So English , uh , Finnish and Italian are Aurora .
F: Yeah .
G: And Spanish and French is something that we can use in addition to Aurora .
G: Uh , well .
F: Yeah ,
F: so Carmen brought the Spanish ,
F: and Stephane brought the French .
C: OK .
C: And ,
C: um ,
C: oh yeah ,
F: Is it French French or Belgian French ?
C: and F: There 's a G: It 's , uh , French French .
C: French French .
E: Like Mexican Spain and Spain .
F: Yeah .
B: Or Swiss .
E: I think that is more important ,
B: Swiss - German .
E: Mexican Spain .
F: Yeah .
E: Because more people F: Yeah ,
F: probably so .
E: Yeah .
F: Yeah .
F: Yeah , Herve always insists that Belgian is i is absolutely pure French ,
F: has nothing to do with F: but he says those those those Parisians talk funny .
G: Yeah , yeah , yeah .
G: They have an accent .
F: Yeah they F: they do , yeah .
F: Yeah . But then he likes Belgian fries too ,
F: so .
F: OK .
C: And then we have , uh , um , broader broader corpus , um , like TIMIT .
C: TIMIT so far ,
E: And Spanish too .
C: right ?
C: Spanish C: Oh , Spanish stories ?
E: Albayzin is the name .
A: What about TI - digits ?
C: Um , TI - digits C: uh all these Aurora f d data p data is from is derived from TI - digits .
A: Uh - huh .
A: Oh . Oh OK .
C: Um , basically , they they corrupted it with , uh , different kinds of noises at different SNR levels .
A: Ah .
A: I see .
C: Yeah .
F: y And I think Stephane was saying there 's there 's some broader s material in the French also ?
G: Yeah ,
G: we cou we could use G: Yeah .
C: OK .
G: The French data .
E: Spanish stories ?
E: No .
C: No .
C: Sp - Not Spanish stories ?
E: No .
F: Spanish E: No . Albayz
C: Spanish something .
E: Yeah .
C: OK .
B: Did the Aurora people actually corrupt it themselves ,
B: or just specify the signal and the signal - t
C: They they corrupted it , um , themselves ,
B: OK .
C: but they also included the the noise files for us ,
C: right ?
C: Or G: Yeah .
C: so we can go ahead and corrupt other things .
F: I 'm just curious , Carmen F: I mean , I couldn't tell if you were joking or F: i Is it is it Mexican Spanish ,
F: or is it E: No no no no .
F: Oh , no , no .
E: No no no no .
F: It 's it 's Spanish from Spain , Spanish .
E: Spanish from Spain .
F: Yeah , OK .
C: From Spain .
F: Alright .
F: Spanish from Spain .
F: Yeah , we 're really covered there
F: now . OK .
F: And the French from France .
C: OK .
G: Yeah ,
G: the No , the French is f yeah , from , uh , Paris ,
C: Oh ,
C: from Paris ,
C: OK .
F: Yeah .
G: OK .
C: And TIMIT 's from lots of different places .
F: From TI .
F: From i It 's from Texas .
F: So may maybe it 's B: From the deep South .
F: So - s so it 's not really from the US either .
F: Is that ? OK .
C: Yeah .
C: Yeah .
C: OK .
C: And , um ,
C: with within the training corporas um , we 're , uh , thinking about , um , training with noise .
C: So ,
C: incorporating the same kinds of noises that , um , Aurora is in incorporating in their , um in their training corpus .
C: Um , I don't think we we 're given the , uh the unseen noise conditions , though ,
C: right ?
F: I think what they were saying was that , um , for this next test there 's gonna be some of the cases where they have the same type of noise as you were given before hand and some cases where you 're not .
C: Like C: Mm - hmm .
G: Mm - hmm .
C: OK .
F: So , presumably , that 'll be part of the topic of analysis of the the test results , is how well you do when it 's matching noise and how well you do where it 's not .
C: Right .
F: I think that 's right .
C: So , I guess we can't train on on the the unseen noise conditions .
F: Well , not if it 's not seen ,
C: Right .
C: If Not if it 's unseen .
F: yeah .
C: Yeah .
F: OK . I mean , i i i i it does seem to me that a lot of times when you train with something that 's at least a little bit noisy it can it can help you out in other kinds of noise
F: even if it 's not matching
F: just because there 's some more variance that you 've built into things .
F: But , but , uh ,
G: Mm - hmm .
F: uh , exactly how well it will work will depend on how near it is to what you had ahead of time .
F: So . OK , so that 's your training corpus ,
G: Mm - hmm .
F: and then your testing corpus ?
C: Um , the testing corporas are , um , just , um , the same ones as Aurora testing .
C: And , that includes , um , the English Spa - um , Italian . Finnish .
E: Finnish .
C: Uh , we ' r we 're gonna get German ,
C: right ?
C: Ge - <comment> At the final test will have German .
F: Well , so ,
F: yeah ,
F: the final test , on a guess , is supposed to be German and Danish ,
G: Uh , yeah .
F: right ?
C: Right .
G: The s
G: yeah ,
G: the Spanish , perhaps ,
C: Spanish .
G: we will have .
C: Oh yeah ,
C: we can we can test on s Spanish .
G: Yeah .
G: But the the Aurora Spanish , I mean .
C: Oh yeah .
C: Mm - hmm .
F: Oh , there 's a there 's Spanish testing in the Aurora ?
G: Uh , not yet ,
G: but , uh , yeah , uh , e
E: Yeah ,
E: it 's preparing .
G: pre they are preparing it ,
G: and , well ,
E: They are preparing .
G: according to Hynek it will be we will have this at the end of November ,
G: or Um .
F: OK ,
F: so , uh ,
G: Yeah F: something like seven things in each , uh each column .
F: So that 's , uh , three hundred and forty - three , uh , different systems that are going to be developed .
F: There 's three of you .
F: Uh ,
F: so that 's
C: Yeah . One hundred each , about .
F: hundred and  hundred and fourteen each .
D: What a what about noise conditions ?
F: What ?
D: w Don't we need to put in the column for noise conditions ?
F: Are you just trying to be difficult ?
D: No ,
D: I just don't understand .
C: Well , th
C: uh ,
F: I 'm just kidding .
C: when when I put these testings on there , I 'm assumi
F: Yeah .
C: There - there 's three three tests .
C: Um ,
C: type - A , type - B , and type - C .
C: And they 're all they 're all gonna be test tested , um , with one training of the HTK system .
C: Um , there 's a script that tests all three different types of noise conditions .
C: Test - A is like a matched noise .
C: Test - B is a is a slightly mismatched .
C: And test - C is a , um , mismatched channel .
D: And do we do all our training on clean data ?
C: Um ,
E: Also , we can clean that .
C: no , no ,
G: No .
C: we 're we 're gonna be , um , training on the noise files that we do have .
F: So ,
F: um F: Yeah ,
F: so I guess the question is how long does it take to do a a training ?
F: I mean , it 's not totally crazy
F: t I mean , these are a lot of these are built - in things
F: and we know we have programs that compute PLP ,
F: we have MSG ,
F: we have JRA
F: you know , a lot of these things will just kind of happen ,
F: won't take uh a huge amount of development ,
F: it 's just trying it out .
F: So , we actually can do quite a few experiments .
F: But how how long does it take , do we think , for one of these <comment> trainings ?
C: Mm - hmm .
C: That 's a good question .
A: What about combinations of things ?
F: Oh yeah ,
F: that 's right .
F: I mean , cuz ,
F: so , for instance , I think the major advantage of MSG C: Oh !
F: Yeah ,
C: Och !
F: good point .
F: A major advantage of MSG , I see , th that we 've seen in the past is combined with PLP .
E: Yeah .
F: Um .
C: Now , this is turning into a four - dimensional cube ?
B: Or you just add it to the features .
E: No .
A: Well , you just select multiple things on the one dimension .
E: Here .
C: Just C: Oh , yeah .
C: OK .
F: Yeah ,
F: so , I mean ,
F: you don't wanna ,
F: uh F: Let 's see ,
F: seven choose two would   be , uh , twenty - one different combinations .
F: Um .
B: It 's not a complete set of combinations , though ,
F: Probably B: right ?
F: What ?
B: It 's not a complete set of combinations , though ,
C: No .
B: right ?
F: Yeah ,
F: I hope not .
F: Yeah ,
F: there 's C: That would be F: Uh ,
F: yeah ,
F: so PLP and MSG I think we definitely wanna try
F: cuz we 've had a lot of good experience with putting those together .
E: Mm - hmm .
F: Um .
F: Yeah .
A: When you do that , you 're increasing the size of the inputs to the net .
A: Do you have to reduce the hidden layer , or something ?
F: Well , so F: I mean ,
F: so i it doesn't increase the number of trainings .
A: No , no ,
A: I 'm I 'm just wondering about number of parameters in the net .
A: Do you have to worry about keeping that the same ,
A: or ?
F: Uh , I don't think so .
B: There 's a computation limit , though ,
B: isn't there ?
F: Yeah ,
F: I mean , it 's just more compu
F: Excuse me ?
B: Isn't there like a limit on the computation load , or d latency , or something like that for Aurora task ?
F: Oh yeah ,
F: we haven't talked about any of that at all ,
F: have we ?
C: No .
F: Yeah ,
F: so , there 's not really a limit .
F: What it is is that there 's there 's , uh it 's just penalty ,
F: you know ?
F: That that if you 're using , uh , a megabyte , then they 'll say that 's very nice ,
F: but , of course , it will never go on a cheap cell phone .
F: Um .
B: OK .
F: And ,
F: u uh , I think the computation isn't so much of a problem .
F: I think it 's more the memory .
F: Uh , and , expensive cell phones , exa expensive hand - helds , and so forth , are gonna have lots of memory .
F: So it 's just that , uh , these people see the the cheap cell phones as being still the biggest market ,
F: so .
B: Mm - hmm .
F: Um .
F: But , yeah ,
F: I was just realizing that , actually , it doesn't explode out ,
F: um F: It 's not really two to the seventh .
F: But it 's but but i i it doesn't really explode out the number of trainings
F: cuz these were all trained individually .
F: Right ?
F: So ,
F: uh , if you have all of these nets trained some place , then , uh , you can combine their outputs and do the KL transformation and so forth
F: and and , uh C: Mm - hmm .
F: So , what it it blows out is the number of uh testings .
F: And , you know and the number of times you do that last part .
F: But that last part , I think , is so has gotta be pretty quick ,
F: so .
F: Uh .
F: Right ?
F: I mean , it 's just running the data through C: Oh .
F: Well , you gotta do the KL transformation ,
A: But wh what about a net that 's trained on multiple languages , though ?
G: Eight y
F: but A: Is that just separate nets for each language then combined ,
E: Necessary to put in .
A: or is that actually one net trained on ?
G: Uh , probably one net .
F: Good question .
G: Well .
G: Uh .
F: One would think one net ,
G: So .
F: but we 've I don't think we 've tested that .
F: Right ?
G: So , in the broader training corpus we can we can use , uh , the three , or , a combination of of two two languages .
E: Database three .
A: In one net .
G: Yeah .
A: Mm - hmm .
F: Yeah ,
F: so , I guess the first thing is if w if we know how much a how long a a training takes ,
F: if we can train up all these these combinations , uh , then we can start working on testing of them individually , and in combination .
F: Right ?
F: Because the putting them in combination , I think , is not as much computationally as the r training of the nets in the first place .
C: Mm - hmm .
F: Right ?
G: Yeah .
F: So y you do have to compute the KL transformation .
F: Uh ,
F: which is a little bit ,
F: but it 's not too much .
G: It 's not too much ,
G: no .
F: Yeah .
F: So it 's G: But Yeah .
G: But there is the testing also , which implies training , uh , the HTK models
E: The the model G: and , well ,
E: the HTK model .
F: Uh , right .
G: it 's F: Right .
G: yeah .
F: So if you do have lots of combinations , it 's G: But it 's it 's it 's not so long .
G: It @ @ G: Yeah .
F: How long does it take for an , uh , HTK training ?
G: It 's around six hours , I think .
E: It depends on the G: For training and testing ,
G: yeah .
E: More than six hours .
G: More .
E: For the Italian ,
E: yes .
E: Maybe one day .
G: One day ?
F: For HTK ?
E: Yeah .
F: Really ?
E: Well .
F: Running on what ?
E: Uh , M MFCC .
F: No ,
F: I 'm sorry ,
F: ru running on what machine ?
E: Uh , Ravioli .
F: Uh ,
F: I don't know what Ravioli is .
F: Is it is it an Ultra - five ,
F: or is it a ?
E: mmm
E: Um . Who is that ?
A: I don't know .
C: I don't know .
E: I don't know .
B: I don't know what a Ravioli is .
E: I don't know .
C: I don't know .
B: We can check really quickly ,
G: Yeah ,
B: I guess .
G: I I think it 's - it 's - it 's not so long
G: because , well , the TI - digits test data is about , uh how many hours ?
G: Uh , th uh , thirty hours of speech , I think ,
F: It 's a few hours .
G: something like that .
B: Hmm .
F: Yeah . Right ,
G: And it
G: p Well .
F: so ,
F: I mean ,
F: clearly , there there 's no way we can even begin to do an any significant amount here unless we use multiple machines .
G: It 's six hours .
F: Right ?
F: So so w we F: I mean there 's plenty of machines here
F: and they 're n they 're often not in in a great great deal of use .
F: So , I mean , I think it 's it 's key that that the that you look at , uh , you know , what machines are fast , what machines are used a lot F: Uh , are we still using P - make ?
F: Is that ?
C: Oh ,
C: I don't know how w how we would P - make this , though .
C: Um .
F: Well , you have a F: I mean , once you get the basic thing set up , you have just all the uh , a all these combinations ,
C: Yeah .
F: right ?
C: Mm - hmm .
F: Um .
F: It 's it 's let 's say it 's six hours or eight hours , or something for the training of HTK .
F: How long is it for training of of , uh , the neural net ?
C: The neural net ?
C: Um .
G: I would say two days .
A: Depends on the corpuses , right ?
E: It depends .
G: Yeah .
B: It s also depends on the net .
E: Depends on the corpus .
C: Yeah .
B: How big is the net ?
E: For Albayzin I trained on neural network ,
E: uh , was , um , one day also .
F: Uh ,
F: but on what machine ?
C: On a SPERT board .
E: Uh .
E: I I think the neural net SPERT .
C: Y you did a you did it on a SPERT board .
F: OK ,
E: Yes .
F: again , we do have a bunch of SPERT boards .
C: Yeah .
F: And I think there there there 's I think you folks are probably go the ones using them right now .
A: Is it faster to do it on the SPERT ,
A: or ?
F: Uh , don't know .
F: Used to be .
C: It 's it 's still a little faster on the
A: Is it ?
C: Yeah ,
C: yeah .
C: Ad - Adam Adam did some testing .
C: Or either Adam or or Dan did some testing
C: and they found that the SPERT board 's still still faster .
C: And the benefits is that , you know , you run out of SPERT and then you can do other things on your your computer ,
A: Mm - hmm .
F: Mm - hmm .
C: and you don't A: Mm - hmm .
F: Yeah .
F: So you could be F: we have quite a few SPERT boards .
F: You could set up , uh , you know , ten different jobs , or something , to run on SPERT different SPERT boards
F: and and have ten other jobs running on different computers .
F: So , it 's got to take that sort of thing ,
F: or or we 're not going to get through any significant number of these .
C: Yeah .
F: So this is F: Yeah , I mean ,
F: I kind of like this because what it F: No F: uh , no , what I like about it is
C: OK .
F: we we we do have a problem
F: that we have very limited time .
F: You know ,
F: so , with very limited time , we actually have really quite a quite a bit of computational resource available