E: I 've given up on trying to do digits .
E: I just don't think that what I have so far makes a Eurospeech paper .
A: Well , I 'm no We may be in the same position ,
A: and I figured we 'll try ,
A: because that 'll at least get us to the point where we have We have this really nice database format that Andreas and I were working out that A: It it 's not very fancy .
A: It 's just a ASCII line by line format ,
A: but it does give you information F: It 's the it 's the spurt format .
A: It Yeah , we 're calling these " spurts " after Chafe .
A: I was trying to find what 's a word for a continuous region with pauses around it ?
C: Hmm .
B: Yeah . I know that th the Telecom people use use " spurt " for that .
C: Good .
A: They do ?
F: Oh .
B: Yes .
A: Oh !
A: Oh .
B: And that 's I mean , I I was using that for a while when I was doing the rate of speech stuff ,
A: I would jus
B: because I because I looked up in some books and I found OK , I wanna find a spurt in which A: Ah , right !
A: It 's just , like , defined by the acoustics .
B: and an because cuz it 's another question about how many pauses they put in between them .
E: Horrible .
A: Right .
B: But how fast do they do the words within the spurt ?
A: Right .
B: Yeah .
G: you know " Burst " also ?
E: It 's gonna A: Well , that 's what we were calling spurt ,
E: Burst .
G: Isn't " burst " is used also ?
A: so E: Spurt has the horrible name overloading with other with hardware at ICSI .
B: Here .
B: Just very locally , yeah .
B: But but that just A: Well , well , Chafe had this wor I think it was Chafe , or somebody had a the word " spurt " originally ,
H: Here @ @ A: and so I C: Actually A: But tha that 's good to know .
A: Was thi it 's Chafe ?
F: So maybe we should talk C: Well , see , I know S Sue wrote about spurts of development .
A: Maybe it was Sue ?
C: But , in any case , I think it 's a good term ,
A: Y
E: Hmm !
C: and , uh A: So we have spurts and we have spurt - ify dot shell and spurt - ify
B: Yeah .
C: And ma maybe maybe Chafe did .
F: Uh .
B: Yeah .
F: So s
C: I know I know Ch - Chafe dealt with A: And then it 's got all it 's a verb now .
G: That 's cool .
F: W uh , w
C: Chafe speaks about intonation units .
A: Yes . Right .
C: But maybe he speaks about spurts as well
F: We
C: and I just don't know .
C: Yeah , go ahead .
F: So what we 're doing E: I 've heard " burst " also .
F: uh , this this is just maybe someone has s some some ideas about how to do it better ,
G: Mmm .
F: but we So we 're taking these , uh , alignments from the individual channels .
F: We 're F: from each alignment we 're producing , uh , one of these CTM files ,
F: which essentially has it 's just a linear sequence of words with the begin times for every word and the duration .
C: Great .
F: And and and of course A: It looks like a Waves label file almost . Right ?
F: Right . But it has one the first column has the meeting name ,
A: It 's just F: so it could actually contain several meetings .
F: Um .
F: And the second column is the channel .
F: Third column is the , um , start times of the words and the fourth column is the duration of the words .
F: And then we 're ,
F: um F: OK . Then we have a messy alignment process where we actually insert into the sequence of words the , uh , tags
F: for , like , where where sentence ends of sentence ,
F: question marks ,
F: um , various other things .
A: Yeah . These are things that we had Don F: Uh .
A: So , Don sort of , um , propagated the punctuation from the original transcriber F: Right .
A: so whether it was , like , question mark or period or , um , you know , comma and things like that ,
A: and we kept the and disfluency dashes uh , kept those in because we sort of wanna know where those are relative to the spurt overlaps F: Mm - hmm .
F: Right .
F: So so those are actually sort of retro - fitted into the time alignment .
A: sp overlaps ,
A: or F: And then we merge all the alignments from the various channels
F: and we sort them by time .
F: And then there 's a then there 's a process where you now determine the spurts .
F: That is Actually , no , you do that before you merge the various channels .
F: So you you id identify by some criterion ,
F: which is pause length F: you identify the beginnings and ends of these spurts ,
F: and you put another set of tags in there to keep those straight .
B: Mm - hmm .
F: And then you merge everything in terms of , you know , linearizing the sequence based on the time marks .
F: And then you extract the individual channels again ,
F: but this time you know where the other people start and end talking F: you know , where their spurts start and end .
F: And so you extract the individual channels , uh , one sp spurt by spurt as it were .
F: Um , and inside the words or between the words you now have begin and end tags for overlaps .
F: So , you you basically have everything sort of lined up and in a form where you can look at the individual speakers and how their speech relates to the other speakers ' speech .
E: Right .
A: Uh , I mean , I think that 's actually really u useful also
F: And A: because even if you weren't studying overlaps , if you wanna get a transcription for the far - field mikes , how are you gonna know which words from which speakers occurred at which times relative to each other ?
A: You have to be able to get a transcript like like this anyway , just for doing far - field recognition .
A: So ,
A: you know , it 's it 's sort of F: Yeah .
A: I thi it 's just an issue we haven't dealt with before ,
A: how you time - align things that are overlapping anyway .
F: So C: That 's wonderful .
E: Well F: And and we A: I mean , i I never thought about it before ,
E: Y yes .
F: In A: but E: I mean , s when I came up with the original data suggested data format based on the transcription graph , there 's capability of doing that sort of thing in there .
A: Right .
C: Mm - hmm .
A: But you can't get it directly from the transcription .
F: Right .
C: Yeah , that 's right .
F: Well , this is this is just A: Yeah , this is like a poor man 's ver formatting version .
A: But it 's , you know It 's clean ,
A: it 's just not fancy .
E: Right .
F: Well , there 's lots of little things .
A: Um .
F: It 's like there 're twelve different scripts which you run
F: and then at the end you have what you want .
F: But , um ,
F: at the very last stage we throw away the actual time information .
F: All we care about is whether that there 's a certain word was overlapped by someone else 's word .
F: So you sort of at that point , you discretize things into just having overlap or no overlap .
F: Because we figure that 's about the level of analysis that we want to do for this paper .
E: Mm - hmm .
F: But if you wanted to do a more fine - grained analysis and say , you know , how far into the word is the overlap , you could do that .
F: It 's just it 'll just require more A: Yeah .
A: Just sort of huge .
F: you know , slightly different C: What 's interesting is it 's exactly what , um , i in discussing with , um , Sue about this ,
A: Yeah .
C: um , she , um , i i i indicated that that you know , that 's very important for overlap analysis .
A: Yeah . It 's it 's nice to know ,
F: Right .
A: and also I think as a human , like , I don't always hear these in the actual order that they occur .
A: So I can have two foreground speakers ,
A: you know , Morgan an and um , Adam and Jane could all be talking ,
A: and I could align each of them to be starting their utterance at the correct time ,
A: and then look where they are relative to each other ,
A: and that 's not really what I heard .
C: And that 's another thing she said .
A: Cuz it 's just hard to do .
C: This is This is Bever 's Bever 's effect ,
C: when where In psy ps psycho - linguistics you have these experiments where people have perceptual biases a as to what they hear ,
A: Y Yeah .
A: It 's sort of C: that that Not the best A: Yeah , you sort of move things around until you get to a low information point
A: and yo then you can bring in the other person .
A: So it 's actually not even possible , I think , for any person to listen to a mixed signal ,
A: even equalize , and make sure that they have all the words in the right order .
C: Mm - hmm .
A: So , I guess , we 'll try to write this Eurospeech paper .
C: Superb .
A: I mean , we will write it .
A: Whether they accept it late or not , I don't know .
A: Um , and the good thing is that we have It 's sort of a beginning of what Don can use to link the prosodic features from each file to each other .
F: Yeah .
B: Yeah . That 's the good thing about these pape
F: Plus , mayb
A: So .
H: Hmm ?
A: i You know , might as well .
A: We - I ju Otherwise we won't get the work done <comment> on our deadline .
F: I don't know , m
F: I mean , u u Jane likes to look at data .
B: Yeah .
F: Maybe , you know , you could you could look at this format and see if you find anything interesting .
F: I don't know .
B: Yeah .
C: Well , what I 'm thinking is B: No , it 's that 's the good thing about these pape paper deadlines and , uh , you know , class projects , and  and things like that ,