A: Yeah .
F: Yeah .
C: Yeah .
F: Mm - hmm .
A: Right .
C: Well , my F: Well th th the other thing that that that yo that you usually don't tell your graduate students is that these deadlines are actually not that , um , you know , strictly enforced ,
B: because you you really get g
C: Yeah .
A: Forces you to do the work .
B: Yeah .
A: Exactly .
E: Strict .
F: because the B: Oh , now it 's out in the public ,
B: this this this secret information .
F: because C: I think we can ha
B: Yeah .
F: bec b Nah A: Right .
E: No .
A: So B: No .
C: Nah .
F: i Because these the conference organizers actually have an interest in getting lots of submissions .
A: Right .
F: I mean , a a monetary interest .
E: Right .
B: Yeah .
F: So  Um .
B: Th - that 's that 's true .
C: And good ones ,
C: good ones ,
F: And good submission
C: which sometimes means a little extra time .
B: That 's F: Right .
F: Well B: That 's true .
F: That 's another issue ,
F: but B: By th by the way , this is totally unfair , you may you may feel ,
B: but the the , uh the morning meeting folks actually have an an extra month or so .
F: Mm - hmm .
D: Yep .
E: Yep . The Aurora there 's a special Aurora F: When A: Uh B: There 's a special Aurora session
B: and the Aurora pe people involved in Aurora have till Ma - uh , early May or something to turn in their paper .
A: Oh .
F: Mmm .
A: Oh .
F: Mmm .
F: Well , then you can just A: Oh , well maybe we 'll submit to s <comment> Actually F: Maybe you can submit the digits paper on e for the Aurora session .
H: Yeah .
D: Yeah .
A: Yeah .
E: Oh , I could !
B: I if it w
A: Yeah .
E: I could submit that to Aurora .
F: Yeah .
B: Well E: That would be pretty pretty B: i it has A: Yeah .
E: S That wouldn't work .
B: No , it wouldn't work .
E: It 's not Aurora .
B: It 's it 's not the Aurora B: I mean , it it 's it 's actually the Aurora task .
A: Maybe they 'll get s
E: Aurora 's very specific .
A: Well , maybe it won't be after this deadline extension .
B: It
F: But but the people I mean ,
F: a a paper that is not on Aurora would probably be more interesting at that point
A: Maybe they 'll F: because everybody 's so sick and tired of the Aurora task .
D: Yeah .
E: Oh , I thought you meant this was just the digits section .
E: I didn't know you meant it was Aurora digits .
B: Yeah .
F: Well , no . If you if you have it 's to if you discuss some relation to the Aurora task ,
F: like if you use the same B: This is not the Aurora task .
B: So they just do a little grep for A: Do uh , d d
F: Um .
A: Do not do not we are not setting a good example .
F: Well , a relation other than negation , maybe ,
A: This is not a F: um .
F: So .
F: I don't know .
A: Anyway .
A: But the good thing is this does E: Well , I I don't know .
E: I mean , you could you could do a paper on what 's wrong with the Aurora task by comparing it to other ways of doing it .
F: How well does an Aurora system do on on you know , on digits collected in a in this environment ?
E: Different way .
E: Yeah .
F: Yeah .
B: Maybe .
F: Maybe .
E: Pretty hokey .
B: I think it 's a littl little far - fetched .
B: Nah , I mean , the thing is Aurora 's pretty closed community .
E: Yep .
B: I mean , you know , the people who were involved in the  the only people who are allowed to test on that are people who who made it above a certain threshold in the first round ,
F: Mm - hmm .
E: It 's very specific .
B: uh w in ninety - nine
B: and it 's it 's sort of a it 's F: Well , that 's maybe why they don't f know that they have a crummy system .
B: not like a F: I mean , a crummy back - end .
F: No , I mean I mean , seriously ,
F: if you if you have a very F: No , I 'm sorry .
A: Uh , <comment> " beep " " bee "
F: No . I didn't mean anybody any particular system .
E: I mean , th
F: I meant this H T K back - end .
F: If they B: Oh , you don't like HTK ?
H: Yeah .
F: I don't h I don't have any stock in HTK or Entropic or anything .
B: No . I mean , this it it 's the HTK that is trained on a very limited amount of data .
E: It 's d it 's very specific .
F: Right .
B: Yeah .
F: But so , if you But maybe you should , you know , consider more using more data ,
F: or I mean B: Oh , yeah . I I really think that that 's true .
F: If yo if you sort of hermetically stay within one task and don't look left and right , then you 're gonna B: And they i i
E: But they they had B: i
B: But E: They had something very specific in mind when they designed it . Right ?
F: Right .
B: Well , u i
E: And so so you can you can argue about maybe that wasn't the right thing to do ,
E: but , you know , they they they had something specific .
B: But , one of the reasons I have Chuck 's messing around with with the back - end that you 're not supposed to touch F: Mm - hmm .
B: I mean , for the evaluations , yes , we 'll run a version that hasn't been touched .
F: Mm - hmm .
B: But , uh , one of the reasons I have him messing around with that , because I think it 's sort of an open question that we don't know the answer to .
B: People always say very glibly that i if you s show improvement on a bad system , that doesn't mean anything ,
B: cuz it may not be  show uh , because , you know , it doesn't tell you anything about the good system .
F: Mm - hmm .
B: And I I 've always sort of felt that that depends .
B: You know , that if some peopl If you 're actually are getting at something that has some conceptual substance to it , it will port .
F: Mm - hmm .
B: And in fact , most methods that people now use were originally tried with something that was not their absolute best system at some level .
B: But of course , sometimes it doesn't , uh , port .
B: So I think that 's that 's an interesting question .
B: If we 're getting three percent error on , uh , u uh , English , uh , nati native speakers , um , using the Aurora system , and we do some improvements and bring it from three to two , do those same improvements bring , uh , th you know , the SRI system from one point three to you know , to point eight ?
F: Hmm .
F: Mm - hmm .
E: Zero .
B: Well .
F: Mmm .
B: You know , so that 's that 's something we can test .
F: Right .
B: So .
B: Anyway .
F: OK .
B: I think we 've  we 've covered that one up extremely well .
C: Mm - hmm .
F: Whew !
B: OK .
B: So , um B: Yeah .
B: So tha so we 'll you know , maybe you guys 'll have have one .
B: Uh , you you and , uh and Dan have have a paper that that 's going in .
D: Yeah .
D: Yeah .
B: You know , that 's that 's pretty solid , on the segmentation stuff .
D: Yeah .
D: I will send you the the final version ,
B: Yeah .
B: And the Aurora folks here will will definitely get something in on Aurora ,
D: which is not F: Actually this this , um So , there 's another paper .
B: so .
F: It 's a Eurospeech paper but not related to meetings .
F: But it 's on digits .
F: So , um ,
F: uh , a colleague at SRI developed a improved version of MMIE training .
B: Uh - huh .
F: And he tested it mostly on digits
F: because it 's sort of a you know , it doesn't take weeks to train it .
B: Right .
F: Um .
F: And got some very impressive results , um , with , you know , discriminative , uh , Gaussian training . Um , you know , like , um , error rates go from I don't know , in very noisy environment , like from ,
F: uh , uh F: I for now I OK , now I have the order of magnit
F: I 'm not sure about the order of magnitude .
F: Was it like from ten percent to eight percent or from e e you know , point you know , from one percent to point eight percent ?
B: H i it got it got better .
F: I mean , it 's a F: It got better .