PhD A: OK , we 're going .
PhD C: Eight , eight ?
PhD D: This is three .
PhD C: Three .
PhD D: Yep . Yep .
Professor B: Test . Hmm . Let 's see . Move it bit . Test ? Test ? OK , I guess it 's alright . So , let 's see . Yeah , Barry 's not here and Dave 's not here . Um , I can say about just q just quickly to get through it , that Dave and I submitted this ASRU .
PhD A: This is for ASRU .
Professor B: Yeah . So . Um . Yeah , it 's it 's interesting . I mean , basically we 're dealing with rever reverberation , and , um , when we deal with pure reverberation , the technique he 's using works really , really well . Uh , and when they had the reverberation here , uh , we 'll measure the signal - to - noise ratio and it 's , uh , about nine DB . So ,
PhD D: Hmm .
Professor B: um ,
PhD A: You mean , from the actual , uh , recordings ?
Professor B: a fair amount of PhD D: k
PhD A: It 's nine DB ?
Professor B: Yeah . Yeah . Um And actually it brought up a question which may be relevant to the Aurora stuff too . Um , I know that when you figured out the filters that we 're using for the Mel scale , there was some experimentation that went on at at , uh at OGI . Um , but one of the differences that we found between the two systems that we were using , {comment} the the Aurora HTK system baseline system {comment} and the system that we were the the uh , other system we were using , the uh , the SRI system , was that the SRI system had maybe a , um , hundred hertz high - pass . And the , uh , Aurora HTK , it was like twenty .
PhD D: Yep . S sixty - four .
Professor B: Uh .
PhD D: S sixty - four .
Professor B: Sixty - four ? Uh .
PhD D: Yeah , if you 're using the baseline .
Professor B: Is that the ba band center ?
PhD D: No , the edge .
Professor B: The edge is really , uh , sixty - four ?
PhD D: Yeah .
Professor B: For some reason , uh , Dave thought it was twenty ,
PhD D: So the , uh , center would be somewhere around like hundred
Professor B: but .
PhD D: and hundred and hundred hundred and maybe it 's like fi hundred hertz .
Professor B: But do you know , for instance , h how far down it would be at twenty hertz ? What the how much rejection would there be at twenty hertz , let 's say ?
PhD D: At twenty hertz .
Professor B: Yeah , any idea what the curve looks like ?
PhD D: Twenty hertz frequency Oh , it 's it 's zero at twenty hertz , right ? The filter ?
PhD C: Yea - actually , the left edge of the first filter is at sixty - four .
PhD D: Sixt - s sixty - four .
PhD C: So PhD D: So anything less than sixty - four is zero .
PhD C: Mmm .
Professor B: It 's actually set to zero ? What kind of filter is that ?
PhD C: Yeah .
PhD D: Yeah .
Professor B: Is this oh , from the from PhD C: It This is the filter bank in the frequency domain that starts at sixty - four .
Professor B: Oh , so you , uh so you really set it to zero , the FFT ?
PhD D: Yeah ,
PhD C: Yeah .
PhD D: yeah . So it 's it 's a weight on the ball spectrum . Triangular weighting .
Professor B: Right . OK . Um OK . So that 's that 's a little different than Dave thought , I think . But but , um , still , it 's possible that we 're getting in some more noise . So I wonder , is it @ @ Was there their experimentation with , uh , say , throwing away that filter or something ? And , uh PhD D: Uh , throwing away the first ?
Professor B: Yeah .
PhD D: Um , yeah , we we 've tried including the full full bank . Right ? From zero to four K .
PhD C: Mm - hmm .
PhD D: And that 's always worse than using sixty - four hertz .
Professor B: Right , but the question is , whether sixty - four hertz is is , uh , too , uh , low .
PhD D: Yeah , I mean , make it a hundred or so ?
Professor B: Yeah .
PhD D: I t I think I 've tried a hundred and it was more or less the same , or slightly worse .
Professor B: On what test set ?
PhD D: On the same , uh , SpeechDat - Car , Aurora .
Professor B: Um , it was on the SpeechDat - Car .
PhD D: Yeah . So I tried a hundred to four K . Yeah .
Professor B: Um ,
PhD D: So it was Professor B: and on and on the , um , um , TI - digits also ?
PhD D: No , no , no . I think I just tried it on SpeechDat - Car .
Professor B: Mmm . That 'd be something to look at sometime because what , um , eh , he was looking at was performance in this room .
PhD D: Mm - hmm .
Professor B: Would that be more like Well , you 'd think that 'd be more like SpeechDat - Car , I guess , in terms of the noise . The SpeechDat - Car is more , uh , sort of roughly stationary , a lot of it . And and TI - digits maybe is not so much as PhD D: Yeah .
PhD C: Mm - hmm .
Professor B: Yeah .
PhD D: Yeah .
Professor B: Mm - hmm . OK . Well , maybe it 's not a big deal . But , um Anyway , that was just something we wondered about . But , um , uh , certainly a lot of the noise , uh , is , uh , below a hundred hertz . Uh , the signal - to - noise ratio , you know , looks a fair amount better if you if you high - pass filter it from this room .
PhD D: Yeah .
Professor B: But , um but it 's still pretty noisy . Even even for a hundred hertz up , it 's it 's still fairly noisy . The signal - to - noise ratio is is is actually still pretty bad .
PhD C: Mm - hmm .
PhD A: Hmm .
Professor B: So , um , I mean , the main the the PhD A: So that 's on th that 's on the f the far field ones though , right ? Yeah .
Professor B: Yeah , that 's on the far field . Yeah , the near field 's pretty good .
PhD A: So wha what is , uh what 's causing that ?
Professor B: Well , we got a a video projector in here , uh , and , uh which we keep on during every every session we record ,
PhD A: Yeah .
Professor B: which , you know , I I w we were aware of
PhD A: Uh - huh .
Professor B: but but we thought it wasn't a bad thing .
PhD A: Yeah .
Professor B: I mean , that 's a nice noise source . Uh , and there 's also the , uh uh , air conditioning .
PhD A: Hmm .
Professor B: Which , uh , you know , is a pretty low frequency kind of thing .
PhD A: Mm - hmm .
Professor B: But but , uh So , those are those are major components , I think ,
PhD A: I see .
Professor B: uh , for the stationary kind of stuff .
PhD A: Mmm .
Professor B: Um , but , um , it , uh I guess , I maybe I said this last week too but it it it really became apparent to us that we need to to take account of noise . And , uh , so I think when when he gets done with his prelim study I think one of the next things we 'd want to do is to take this , uh uh , noise , uh , processing stuff and and , uh uh , synthesize some speech from it .
PhD A: When are his prelims ?
Professor B: And then Um , I think in about , um , a little less than two weeks .
PhD A: Oh . Wow .
Professor B: Yeah . Yeah . So . Uh , it might even be sooner . Uh , let 's see , this is the sixteenth , seventeenth ? Yeah , I don't know if he 's before It might even be in a week .
PhD A: So , I
Professor B: A week ,
PhD A: Huh . I I guessed that they were gonna do it some time during the semester
Professor B: week and a half .
PhD A: but they 'll do it any time , huh ?
Professor B: They seem to be Well , the semester actually is starting up .
PhD A: Is it already ?
Professor B: Yeah , the semester 's late late August they start here .
PhD A: Yikes .
Professor B: So they do it right at the beginning of the semester .
PhD A: Yeah .
Professor B: Yeah . So , uh Yep . I mean , that that was sort of one I mean , the overall results seemed to be first place in in in the case of either , um , artificial reverberation or a modest sized training set . Uh , either way , uh , i uh , it helped a lot . And But if you had a a really big training set , a recognizer , uh , system that was capable of taking advantage of a really large training set I thought that One thing with the HTK is that is has the as we 're using the configuration we 're using is w s is being bound by the terms of Aurora , we have all those parameters just set as they are . So even if we had a hundred times as much data , we wouldn't go out to , you know , ten or t or a hundred times as many Gaussians or anything . So , um , it 's kind of hard to take advantage of of of big chunks of data . Uh , whereas the other one does sort of expand as you have more training data .
PhD C: Mm - hmm .
PhD D: Mmm , yeah .
Professor B: It does it automatically , actually . And so , um , uh , that one really benefited from the larger set . And it was also a diverse set with different noises and so forth . Uh , so , um , that , uh that seemed to be So , if you have that that better recognizer that can that can build up more parameters , and if you , um , have the natural room , which in this case has a p a pretty bad signal - to - noise ratio , then in that case , um , the right thing to do is just do u use speaker adaptation . And and not bother with with this acoustic , uh , processing . But I think that that would not be true if we did some explicit noise - processing as well as , uh , the convolutional kind of things we were doing .
PhD C: Mm - hmm .
Professor B: So . That 's sort of what we found .
PhD D: Hmm .
PhD A: I , um  uh , started working on the uh Mississippi State recognizer . So , I got in touch with Joe and and , uh , from your email and things like that .
PhD D: Oh , OK .
PhD A: And , uh , they added me to the list uh , the mailing list .
PhD D: OK , great .
PhD A: And he gave me all of the pointers and everything that I needed . And so I downloaded the , um There were two things , uh , that they had to download . One was the , uh , I guess the software . And another wad was a , um , sort of like a sample a sample run . So I downloaded the software and compiled all of that . And it compiled fine .
PhD D: Eight .
PhD A: No problems .
PhD D: Oh , eh , great .
PhD A: And , um , I grabbed the sample stuff but I haven't , uh , compiled it .
PhD D: That sample was released only yesterday or the day before , right ?
PhD A: No Well , I haven't grabbed that one yet . So there 's two .
PhD D: Oh , there is another short sample set PhD A: There was another short one , yeah .
PhD D: o o sample .
PhD A: And so I haven't grabbed the latest one that he just , uh , put out yet .
PhD D: OK . Oh , OK . F Yeah , OK .
PhD A: So . Um , but , the software seemed to compile fine and everything , so . And , um , So .
Professor B: Is there any word yet about the issues about , um , adjustments for different feature sets or anything ?
PhD A: No , I I d You asked me to write to him and I think I forgot to ask him about that . Or if I did ask him , he didn't reply .
Professor B: Yeah .
PhD A: I I don't remember yet . Uh , I 'll I 'll d I 'll double check that and ask him again .
Professor B: Yeah . Yeah , it 's like that that could r turn out to be an important issue for us .
PhD D: Hmm . Mmm .
PhD A: Yeah . Yeah .
Professor B: Yeah .
PhD D: Cuz they have it PhD A: Maybe I 'll send it to the list . Yeah .
PhD D: Cuz they have , uh , already frozen those in i insertion penalties and all those stuff is what I feel . Because they have this document explaining the recognizer .
PhD A: Uh - huh .
PhD D: And they have these tables with , uh , various language model weights , insertion penalties .
PhD A: OK , I haven't seen that one yet .
PhD D: u
PhD A: So .
PhD D: Uh , it 's th it 's there on that web .
PhD A: OK .
PhD D: And , uh , on that , I mean , they have run some experiments using various insertion penalties and all those PhD A: And so they 've picked the values .
PhD D: Yeah , I think they pi p
PhD A: Oh , OK .
PhD D: yeah , they picked the values from PhD A: OK .
Professor B: For r w what test set ?
PhD D: Uh , p the one that they have reported is a NIST evaluation , Wall Street Journal .
Professor B: But that has nothing to do with what we 're testing on , right ?
PhD C: Mm - hmm .
PhD D: You know . No . So they 're , like um So they are actually trying to , uh , fix that those values using the clean , uh , training part of the Wall Street Journal . Which is I mean , the Aurora . Aurora has a clean subset .
Professor B: Right .
PhD D: I mean , they want to train it and then this they 're going to run some evaluations .
Professor B: So they 're set they 're setting it based on that ?
PhD D: Yeah .
Professor B: OK . So now , we may come back to the situation where we may be looking for a modification of the features to account for the fact that we can't modify these parameters .
PhD A: Yeah .
Professor B: But , um ,
PhD D: Yeah .
Professor B: uh but it 's still worth , I think , just since you know , just chatting with Joe about the issue .
PhD A: Yeah , OK . Do you think that 's something I should just send to him
Professor B: Um PhD A: or do you think I should send it to this there 's an a m a mailing list .
Professor B: Well , it 's not a secret . I mean , we 're , you know , certainly willing to talk about it with everybody , but I think I think that , um um , it 's probably best to start talking with him just to PhD A: OK .
Professor B: Uh @ @ {comment} you know , it 's a dialogue between two of you about what you know , what does he think about this and what what you know what could be done about it .
PhD A: Yeah . OK .
Professor B: Um , if you get ten people in involved in it there 'll be a lot of perspectives based on , you know , how PhD A: Yeah .
Professor B: you know .
PhD A: Right .
Professor B: Uh But , I mean , I think it all should come up eventually ,
PhD A: OK .
Professor B: but if if if there is any , uh , uh , way to move in a way that would that would , you know , be more open to different kinds of features . But if if , uh if there isn't , and it 's just kind of shut down and and then also there 's probably not worthwhile bringing it into a larger forum where where political issues will come in .
PhD A: Yeah . OK .
PhD D: Oh . So this is now it 's it 's compiled under Solaris ?
PhD A: Yeah .
PhD D: Yeah , OK .
PhD A: Yep .
PhD D: Because he there was some mail r saying that it 's may not be stable for Linux and all those .
PhD A: Yeah . Yeah , i that was a particular version .
PhD D: SUSI
PhD A: Yeah , SUSI or whatever it was
PhD D: yeah . Yeah , yeah .
PhD A: but we don't have that .
PhD D: Yeah , OK .
PhD A: So . Should be OK .
PhD D: OK , that 's fine .
PhD A: Yeah , it compiled fine actually .
PhD D: Yeah .
PhD A: No no errors . Nothing . So .
Professor B: Uh , this is slightly off topic
PhD D: That 's good .
Professor B: but , uh , I noticed , just glancing at the , uh , Hopkins workshop , uh , web site that , uh , um one of the thing I don't know Well , we 'll see how much they accomplish , but one of the things that they were trying to do in the graphical models thing was to put together a a , uh , tool kit for doing , uh r um , arbitrary graphical models for , uh , speech recognition .
PhD A: Hmm .
Professor B: So And Jeff , uh the two Jeffs were
PhD A: Who 's the second Jeff ?
Professor B: Uh Oh , uh , do you know Geoff Zweig ?
PhD A: No .
Professor B: Oh . Uh , he he , uh he was here for a couple years
PhD A: Oh , OK .
Professor B: and he , uh got his PHD . He And he 's , uh , been at IBM for the last couple years .
PhD A: Oh , OK .
Professor B: So .
PhD A: Wow . That would be neat .
Professor B: Uh , so he did he did his PHD on dynamic Bayes - nets , uh , for for speech recognition . He had some continuity built into the model , presumably to handle some , um , inertia in the in the production system , and , um PhD A: Hmm .
Professor B: So .
PhD D: Hmm .