A: OK ,
A: we 're recording .
F: We can say the word " zero " all we want ,
G: I 'm doing some
F: but just G: square brackets ,
G: coffee sipping ,
G: square brackets .
B: That 's not allowed , I think .
C: Cur - curly brackets .
E: Is that voiced or unvoiced ?
B: Curly brackets .
F: Curly brackets .
A: Curly brackets .
A: Right .
F: Well ,
B: Oops .
F: correction for transcribers .
G: Mmm ! <comment> Gar - darn !
F: Yeah .
C: Channel two .
A: Do we use square brackets for anything ?
C: Yeah .
E: These poor transcribers .
C: Uh F: u
C: Not ri not right now .
C: I mean No .
D: There 's gonna be some zeros from this morning 's meeting because I noticed that
F: u
D: Barry , I think maybe you turned your mike off before the digits were D: Oh ,
D: was it during digits ?
D: Oh , so it doesn't matter .
F: Yeah .
B: So it 's not it 's not that bad if it 's at the end ,
A: It 's still not a good idea .
B: but it 's in the beginning , it 's bad .
D: Yeah .
D: Yeah .
A: Yeah ,
A: you wanna you wanna keep them on so you get good noise A: noise floors ,
C: That 's interesting .
A: through the whole meeting .
F: Uh ,
F: I probably just should have left it on .
C: Hmm .
F: Yeah I did have to run ,
F: but E: Is there any way to change that in the software ?
A: Change what in the software ?
E: Where like you just don't like if you if it starts catching zeros , like in the driver or something E: in the card , or somewhere in the hardware E: Where if you start seeing zeros on w across one channel , you just add some random , @ @ <comment> noise floor E: like a small noise floor .
A: I mean certainly we could do that ,
A: but I don't think that 's a good idea .
A: We can do that in post - processing if if the application needs it .
E: Yeah .
F: Well , I u I actually don't know what the default <comment> is anymore as to how we 're using the the front - end stuff but for for when we use the ICSI front - end ,
B: Manual post - processing .
F: but um ,
A: As an argument .
F: there is an there is an o an option in in RASTA ,
F: which ,
F: um , in when I first put it in , uh , back in the days when I actually wrote things , uh , I did actually put in a random bit or so that was in it ,
E: OK .
F: but then I realized that putting in a random bit was equivalent to adding uh adding flat spectrum ,
E: Right .
F: and it was a lot faster to just add a constant to the  to the spectrum . So then I just started doing that
E: Mmm .
F: instead of calling " rand " <comment> or something ,
E: OK .
E: Right .
F: so .
F: So it d it does that .
F: Gee ! Here we all are !
A: Uh ,
A: so the only agenda items were Jane was Jane wanted to talk about some of the IBM transcription process .
F: There 's an agenda ?
A: I sort of condensed the three things you said into that .
A: And then just I only have like , this afternoon and maybe tomorrow morning to get anything done before I go to Japan for ten days .
A: So if there 's anything that n absolutely , desperately needs to be done , you should let me know now .
F: Uh , and you just sent off a Eurospeech paper ,
F: so .
G: Right .
G: I hope they accept it .
G: I mean , I F: Right .
G: both actu as as a submission and  you know , as a paper .
G: Um G: but A: Well yeah , you sent it in late .
F: Yeah , I guess you first you have to do the first one ,
A: Yeah .
F: and then F: Yeah .
G: We actually exceeded the delayed deadline by o another day ,
G: so .
B: Oops .
F: Oh they they had some extension that they announced or something ?
G: Well yeah .
G: Liz had sent them a note saying " could we please have another " <comment> I don't know , " three days " or something ,
G: and they said yes .
D: And then she said " Did I say three ?
D: I meant four . "
A: Oh ,
A: that was the other thing
A: uh ,
G: But u
A: uh , Dave Gelbart sent me email , I think he sent it to you too , <comment> that um , there 's a special topic , section in si in Eurospeech on new , corp corpors corpora .
A: And it 's not due until like May fifteenth .
F: Oh this isn't the Aurora one ?
F: It 's another one ?
A: No .
B: No
A: It 's a different one .
B: it 's E: Huh !
B: Yeah .
B: Yeah .
A: And uh ,
F: Oh !
B: I got this mail from A: I s forwarded it to Jane as I thought being the most relevant person .
A: Um A: So , I thought it was highly relevant F: That 's C: Yeah I 'm C: Yeah .
C: I think so too .
A: have you did you look at the URL ?
C: Um , I haven't gotten over to there yet ,
C: but what our discussion yesterday , I really I I wanna submit one .
A: Mm - hmm .
B: Was this SmartKom message ?
C: Yeah .
B: I think Christoph Draxler sent this ,
C: And , you offered to to join me , if you want me to .
B: yeah .
A: I 'll help ,
A: but obviously I can't , really do , most of it ,
C: Yeah .
G: I think several people sent this ,
C: Yeah , that 's right .
B: Yeah .
A: so .
G: yeah .
C: Uh - huh .
A: But any any help you need I can certainly provide .
F: Well ,
G: Yeah .
F: that 's that 's a great idea .
G: Well G: there there were some interesting results in this paper , though .
G: For instance that Morgan uh , accounted for fifty - six percent of the Robustness meetings in terms of number of words .
A: Wow .
C: In in terms of what ?
G: Number of words .
C: In term
C: One ?
C: Wow !
C: OK .
A: That 's just cuz he talks really fast .
C: Do you mean ,
F: n No .
B: Oh .
B: Short words .
C: because A: I know
C: is it partly , eh , c correctly identified words ?
C: Or is it G: No . Well , according to the transcripts .
C: or just overall volume ?
F: Yeah .
A: But re well regardless . I think it 's he 's he 's in all of them ,
C: Oh . OK .
G: I mean , we didn't mention Morgan by name
A: and he talks a lot .
G: we just F: Well we have now ,
A: One participant .
G: We we we something about F: but A: Did you identify him as a senior member ?
G: No ,
G: we as identify him as the person dominating the conversation .
F: Well .
A: Yeah .
C: OK .
F: I mean I get these AARP things ,
F: but I 'm not se really senior yet ,
G: Right
F: but G: Hmm .
F: Um ,
F: but uh , other than that delightful result , what was the rest of the paper about ?
G: Um ,
G: well it was about it had three sections
F: You sent it to me
F: but I haven't seen it yet .
G: uh G: three kinds of uh results , if you will .
G: Uh , the one was that the just the the amount of overlap
A: The good , the bad , and the ugly .
G: um ,
G: s in terms of in terms of number of words
G: and also we computed something called a " spurt " ,
G: which is essentially a stretch of speech with uh , no pauses exceeding five hundred milliseconds .
G: Um , and we computed how many overlapped i uh spurts there were and how many overlapped words there were . Um , for four different corpora ,
G: the Meeting Recorder meetings ,
G: the Robustness meetings
G: Switchboard
G: and CallHome ,
G: and , found and sort of compared the numbers .
G: Um ,
G: and found that the ,
G: uh ,
G: you know , as you might expect the Meeting Recorder meetings had the most overlap
G: uh , but next were Switchboard and CallHome ,
G: which both had roughly the same ,
G: almost identical in fact ,
G: and the Robustness meetings were had the least ,
G: so G: One sort of unexpected result there is that uh two - party telephone conversations have about the same amount of overlap ,
A: I 'm surprised .
G: sort of in gen you know order of magnitude - wise as , uh as face - to - face meetings with multiple A: I have I had better start changing all my slides !
G: Yeah .
G: Also , I in the Levinson , the pragmatics book , <comment> in you know , uh , textbook , there 's I found this great quote where he says you know you know , how people it talks about how uh how how people are so good at turn taking ,
C: Mm - hmm .
C: Yeah .
C: Yeah .
G: and so they 're so good that generally , u the overlapped speech does not is less than five percent .
C: Oh ,
C: that 's interesting .
G: So ,
C: Yeah .
G: this is way more than five percent .
E: Did he mean face like face - to - face ?
E: Or ?
G: Well , in real conversations ,
G: everyday conversations .
E: Hmm .
G: It 's s what these conversation analysts have been studying for years and years there .
C: Mm - hmm .
C: Mm - hmm .
C: Well , of course , no , it doesn't necessarily go against what he said ,
B: But C: cuz he said " generally speaking " . In order to to go against that kind of a claim you 'd have to big canvassing .
A: Hmm .
B: And in f
G: Well , he he made a claim A: Well G: Well F: Yeah , we we have pretty limited sample here .
B: But B: Five percent of time or five percent of what ?
C: Yeah .
A: Yeah , I was gonna ask that too .
G: Well it 's time .
B: Yeah .
C: Exactly .
G: So  but still but still u
C: It 's i it 's not against his conclusion ,
B: Yeah ,
B: so C: it just says that it 's a bi bell curve , and that , you have something that has a nice range , in your sampling .
G: Yeah .
G: So there are slight G: There are differences in how you measure it ,
G: but still it 's  You know , the difference between um between that number and what we have in meetings ,
G: which is more like , you know , close to in meetings like these , uh you know , close to twenty percent .
C: Mm - hmm .
F: But what was it like , say , in the Robustness meeting , for instance ?
C: Mm - hmm .
G: That A: But G: Robustness meeting ?
G: It was about half of the r
G: So , in terms of number of words , it 's like seventeen or eigh eighteen percent for the Meeting Recorder meetings and about half that for , uh , the Robustness .
F: Maybe ten percent ?
A: But I don't know if that 's really a fair way of comparing between , multi - party , conversations and two - party conversations .
B: Then then then you have to A: Yeah .
A: I I I don't know .
A: I mean that 's just something D: Yeah , I just wonder if you have to normalize by the numbers of speakers or something .
C: Yeah .
B: Then Yeah , then normalize by by something like that ,
G: Well , we didn't get to look at that ,
C: Yeah ,
C: that 's a good point .
B: yeah .
C: Yeah .
G: but this obvious thing to see if if there 's a dependence on the number of uh participants .
C: Good idea .
A: I mean A: I bet there 's a weak dependence .
B: Yeah .
A: I 'm sure it 's it 's not a real strong one .
G: Right .
A: Right ?
D: Cuz not everybody talks .
A: Because you
G: Right .
A: Right .
D: Yeah .
A: You have a lot of a lot of two - party , subsets within the meeting .
G: Right .
C: Uh - huh .
A: Well regardless G: So A: it 's an interesting result regardless .
G: Right .
G: And and and then and we also d computed this both with and without backchannels ,
C: Yes , that 's right .
C: Mm - hmm .
G: so you might think that backchannels have a special status because they 're essentially just A: Uh - huh .
A: So , did we all said " uh - huh " and nodded at the same time ,
G: R right .
G: But , even if you take out all the backchannels A: so .
G: so basically you treat backchannels l as nonspeech , as pauses ,
A: Mm - hmm .
F: Mm - hmm .
G: you still have significant overlap .
G: You know , it goes down from maybe For Switchboard it goes down from I don't know f um <comment> I don't know f fourteen percent of the words to maybe uh I don't know , eleven percent or something G: it 's it 's not a dramatic change ,
G: so it 's A: Mm - hmm .
G: Anyway ,
G: so it 's uh G: That was that was one set of results ,
G: and then the second one was just basically the  the stuff we had in the in the HLT paper on how overlaps effect the recognition performance .
C: Hmm .
A: Nope .
A: Right .
F: Mm - hmm .
G: And we rescored things um , a little bit more carefully .
G: We also fixed the transcripts in in numerous ways .
G: Uh , but mostly we added one one number , which was
G: what if you uh , basically score ignoring all G: So so the the conjecture from the HLT results was that most of the added recognition error is from insertions due to background speech .
G: So , we scored all the recognition results , uh , in such a way that the
G: uh A: Oh by the way , who 's on channel four ?
B: Yeah .
A: You 're getting a lot of breath .
E: That 's B: I j was just wondering .
B: Yeah .
E: That 's me .
G: uh , well Don 's been working hard .
E: That 's right .
G: OK ,
G: so  so if you have the foreground speaker speaking here , and then there 's some background speech , may be overlapping it somehow ,
G: um , and this is the time bin that we used ,
G: then of course you 're gonna get insertion errors here and here .
A: Right .
G: Right ?
G: So we scored everything ,
G: and I must say the NIST scoring tools are pretty nice for this ,
G: where you just basically ignore everything outside of the , uh , region that was deemed to be foreground speech .
G: And where that was we had to use the t forced alignment , uh , results from s for G: so G: That 's somewhat that 's somewhat subject to error ,
G: but still we we  Uh , Don did some ha hand - checking
G: and and we think that based on that , we think that the results are you know , valid ,
G: although of course , some error is gonna be in there .
G: But basically what we found is after we take out these regions so we only score the regions that were certified as foreground speech , <comment> the recognition error went down to almost uh , the level of the non - overlapped speech .
G: So that means that even if you do have background speech , if you can somehow separate out or find where it is , uh , the recognizer does a good job ,
A: That 's great .
B: Yeah .
G: even though there is this back
A: Yeah , I guess that doesn't surprise me ,
A: because , with the close - talking mikes , the the signal will be so much stronger .
G: Right .
G: Right .
G: Mm - hmm .
F: Mm - hmm .
G: Um ,
G: so A: What what sort of normalization do you do ?
G: Uh , well , we just @ @ <comment> we do u you know , vit
A: I mean in you recognizer ,
A: in the SRI recognizer .
G: Well , we do uh , VTL  vocal tract length normalization ,
G: w and we uh you know , we we uh , make all the features have zero mean and unit variance .
F: And A: Over an entire utterance ?
G: Over over the entire c over the entire channel .
A: Or windowed ?
B: Don't train G: Over the G: but you know .
A: Hmm .
G: Um , now we didn't re - align the recognizer for this .
G: We just took the old G: So this is actually a sub - optimal way of doing it ,
F: Right .
G: right ?
A: Right .
G: So we took the old recognition output
G: and we just scored it differently .
G: So the recognizer didn't have the benefit of knowing where the foreground speech a start
F: Were you including the the lapel in this ?
G: Yes .
F: And did the did did the la did the the problems with the lapel go away also ?
G: Um , it Yeah .
F: Or F: fray for for insertions ?
G: It u not per I mean , not completely ,
G: but yes ,
G: dramatically .
F: Less so .
G: So we have to
G: um F: I mean , you still G: Well I should bring the should bring the table with results .
G: Maybe we can look at it Monday .
F: I would presume that you still would have somewhat higher error with the lapel for insertions than G: Yes .
G: It 's It 's G: Yes .
F: Yeah .
G: Yeah .
F: Cuz again , looking forward to the non - close miked case , I think that we s still G: Mm - hmm .
A: I 'm not looking forward to it .
F: i it 's the high signal - to - noise ratio
G: Right .
F: here that that helps you .
G: u s Right .
G: So G: so that was number that was the second set of uh , the second section .
G: And then , the third thing was , we looked at ,  uh , what we call " interrupts " ,
G: although that 's that may be a misnomer ,
G: but basically we looked at cases where G: Uh , so we we used the punctuation from the original transcripts
G: and we inferred the beginnings and ends of sentences .
G: So ,
G: you know C: Di - did you use upper - lower case also , or not ?