B: But but ,
G: Mm - hmm .
B: uh ,
B: no , it 's the electronics .
B: And they and G: Mm - hmm .
B: then there 's amplification afterwards .
B: And you can get ,
B: I think it was B: I think it was in the Wall Street Journal data that that B: I can't remember , one of the DARPA things .
B: There was this big DC - DC offset
A: Mm - hmm .
B: we didn't we didn't know about for a while , while we were messing with it .
B: And we were getting these terrible results .
B: And then we were talking to somebody and they said , " Oh , yeah . Didn't you know ?
B: Everybody knows that .
B: There 's all this DC offset in th "
B: So , yes .
B: You can have DC offset in the data .
G: Oh , OK .
G: OK .
B: Yeah .
A: So was that was that everything , Dave ?
E: Oh .
E: And I also , um , did some experiments about normalizing the phase .
E: Um .
E: So I c I came up with a web page that people can take a look at .
E: And , um ,
E: the interesting thing that I tried was , um , Adam and Morgan had this idea ,
E: um , since my original attempts to , um , take the mean of the phase spectra over time and normalize using that , by subtracting that off , didn't work . Um , so , well , that we thought that might be due to , um , problems with , um , the arithmetic of phases .
E: They they add in this modulo two pi way
E: and ,
E: um ,
E: there 's reason to believe that that approach of taking the mean of the phase spectrum wasn't really mathematically correct .
E: So , what I did instead is I took the mean of the FFT spectrum without taking the log or anything , and then I took the phase of that ,
E: and I subtracted that phase off
E: to normalize .
E: But that , um , didn't work either .
B: See , we have a different interpretation of this .
B: He says it doesn't work .
B: I said , I think it works magnificently ,
B: but just not for the task we intended .
B: Uh , it gets rid of the speech .
F: Uh , gets rid of the speech .
A: What does it leave ?
B: Uh , it leaves you know , it leaves the junk .
B: I mean , I I think it 's it 's tremendous .
F: Oh , wow .
B: You see , all he has to do is go back and reverse what he did before ,
B: and he 's really got something .
A: Well , could you take what was left over and then subtract that ?
B: Ex - exactly .
G: Yeah .
B: Yeah , you got it .
F: Yeah .
B: So , it 's it 's a general rule .
G: Oh , it 's B: Just listen very carefully to what I say
B: and do the opposite .
B: Including what I just said .
E: And , yeah , that 's everything .
A: All set ?
A: Do you want to go , Stephane ?
C: Um . Yeah .
C: Maybe , concerning these d still , these meeting digits .
C: I 'm more interested in trying to figure out what 's still the difference between the SRI system and the Aurora system .
C: And C: Um .
C: Yeah . So , I think I will maybe train , like , gender - dependent models ,
C: because this is also one big difference between the two systems .
C: Um ,
C: the other differences were the fact that maybe the acoustic models of the SRI are more SRI system are more complex .
C: But , uh , Chuck , you did some experiments with this
C: and
A: It didn't seem to help in the HTK system .
C: it was hard t to to have some exper some improvement with this .
C: Um .
B: Well , it sounds like they also have B: he he 's saying they have all these , uh , uh , different kinds of adaptation .
C: Mm - hmm .
B: You know , they have channel adaptation .
C: Yeah .
B: They have speaker adaptation .
C: Right .
C: Yeah .
F: Yeah .
B: Yeah . Yeah .
A: Well , there 's also the normalization .
A: Like they do ,
A: um A: I 'm not sure how they would do it when they 're working with the digits ,
C: The vocal tr
A: but , like , in the Switchboard data , there 's , um conversation - side normalization for the non - C - zero
A: components ,
C: Yeah .
C: Yeah . This is another difference .
C: Their normalization works like on on the utterance levels .
A: Mm - hmm .
C: But we have to do it C: We have a system that does it on - line .
C: So , it might be A: Right .
C: it might be better with C: it might be worse if the channel is constant ,
A: Yeah .
C: or C: Nnn .
G: And the acoustic models are like - k triphone models or or is it the whole word ?
C: SRI it 's it 's tr
F: SRI .
G: Yeah .
C: Yeah .
C: I guess it 's triphones .
G: It 's triphone .
B: I think it 's probably more than that .
C: Huh .
B: I mean , so they they have I I thin think they use these , uh , uh , genone things .
B: So there 's there 's these kind of , uh , uh , pooled models
B: and and they can go out to all sorts of dependencies .
G: Oh . It 's like the tied state .
B: So .
A: Mm - hmm .
B: They have tied states
B: and I think B: I I I don't real I 'm talk I 'm just guessing here .
G: OK .
B: But I think I think they they don't just have triphones .
B: I think they have a range of of , uh , dependencies .
C: Mm - hmm .
G: Mm - hmm .
C: Mm - hmm .
F: Hmm .
C: And C: Yeah .
C: Well .
C: Um .
C: Well , the first thing I that I want to do is just maybe these gender things .
C: Uh .
C: And maybe see with Andreas if C: Well , I I don't know how much it helps , what 's the model .
A: So A: so the n stuff on the numbers you got , the two point seven , is that using the same training data that the SRI system used and got one point two ?
C: That 's right .
C: So it 's the clean TI - digits training set .
A: So exact same training data ?
C: Right .
C: Mm - hmm .
A: OK .
C: I guess you used the clean training set .
E: Right .
E: For with the SRI system C: Mm - hmm .
C: Well .
E: You know , the the Aurora baseline is set up with these , um  this version of the clean training set that 's been filtered with this G - seven - one - two filter ,
E: and ,
E: um , to train the SRI system on digits S - Andreas used the original TI - digits ,
E: um , under U doctor - speech data TI - digits ,
E: which don't have this filter .
E: But I don't think there 's any other difference .
C: Mm - hmm .
C: Mm - hmm .
C: Yeah .
B: So is that ? Uh , are are these results comparable ?
B: So you you were getting with the , uh , Aurora baseline something like two point four percent on clean TI - digits , when , uh , training the SRI system with clean TR digits <comment> TI - digits .
B: Right ?
E: Um . Uh - huh .
B: And B: Yeah .
B: And , so , is your two point seven comparable ,
B: where you 're , uh , uh , using , uh , the submitted system ?
C: Yeah .
C: I think so .
C: Yeah .
B: OK .
C: Mm - hmm .
B: So it 's about the same ,
E: W w it was one one point two
B: maybe a little worse .
C: Ye
E: with the SRI system ,
E: I B: I 'm sorry .
C: Yeah .
C: The complete SRI system is one point two .
B: You you were HTK .
C: Yeah .
B: Right ?
B: OK .
C: Mm - hmm .
B: That 's right .
B: So B: OK ,
B: so the comparable number then , uh for what you were talking about then , since it was HTK , would be the um , two point f
C: It was four point something .
C: Right ?
C: The HTK system with , uh , b
E: D d
B: Oh , right , right , right , right .
E: Do you mean the b ?
C: MFCC features E: The baseline Aurora - two system , trained on TI - digits , tested on Meeting Recorder near ,
E: I think we saw in it today ,
E: and it was about six point six percent .
C: Oh .
B: Right . Right , right , right .
B: OK .
B: Alright .
B: So C: So C: Yeah .
C: The only difference is the features , right now ,
B: He 's doing some different things .
C: between this and B: Yes .
B: OK ,
B: good .
B: So they are helping .
C: Mm - hmm .
B: That 's good to hear .
C: They are helping .
B: Yeah .
C: Yeah .
C: Um .
C: Yeah .
C: And another thing I I maybe would like to do is to just test the SRI system that 's trained on Macrophone C: test it on , uh , the noisy TI - digits ,
B: Yeah .
C: cuz I 'm still wondering where this improvement comes from .
C: When you train on Macrophone , it seems better on meeting digits .
C: But I wonder if it 's just because maybe Macrophone is acoustically closer to the meeting digits than than TI - digit is ,
C: which is C: TI - digits are very clean recorded digits
B: Mm - hmm .
C: and C: Uh ,
A: You know , it would also be interesting to see , uh to do the regular Aurora test ,
C: f s
A: um ,
A: but use the SRI system instead of HTK .