PhD G: but Postdoc E: Yep .
PhD G: Great . Now why do we what 's the reason for having like the point five have the " NUMS " on it ? Is that just like when they 're talking about their data or something ?
Postdoc E: This is more because PhD G: Or Postdoc E: Yeah . Oh these are all these , the " NUMS point " , this all where they 're saying " point " something or other .
PhD G: These are all like inside the spontaneous Postdoc E: And the other thing too is for readability of the transcript . I mean if you 're trying to follow this while you 're reading it it 's really hard to read , you know eh , " so in the data column five has " , you know , " one point five compared to seventy nine point six " , it 's like when you see the words it 's really hard to follow the argument . And this is just really a a way of someone who would handle th the data in a more discourse - y way to be able to follow what 's being said .
Grad D: Label it .
PhD G: Oh OK .
Postdoc E: So this is where Chuck 's , um , overall h architecture comes in ,
PhD G: I see .
Postdoc E: where we 're gonna have a master file of the channelized data . Um , there will be scripts that are written to convert it into these t these main two uses and th some scripts will take it down th e into a f a for ta take it to a format that 's usable for the recognizer an uh , other scripts will take it to a form that 's usable for the for linguistics an and discourse analysis . And , um , the implication that that I have is that th the master copy will stay unchanged . These will just be things that are generated ,
Grad D: Right
Postdoc E: and e by using scripts .
PhD G: OK .
Grad D: Master copies of superset .
Postdoc E: When things change then the the script will cham change but the but there won't be stored copies of in different versions of things .
Grad D: Good .
PhD G: So , I guess I 'd have one request here which is just , um , maybe to make it more robust , th that the tag , whatever you would choose for this type of " NUMS " {comment} where it 's inside the spontaneous speech , is different than the tag that you use for the read speech .
PhD B: Right . Right . That would argue for changing the other ones to be " digits " or something .
PhD G: Um , that way w if we make a mistake parsing , or something , we don't see the " point five " , or or it 's not there , then we
PhD B: Mm - hmm .
PhD G: a Just an And actually for things like " seven eighths " , or people do fractions too I guess , you maybe you want one overall tag for sort of that would be similar to that ,
Postdoc E: Except PhD G: or As long as they 're sep as they 're different strings that we that 'll make our p sort of processing more robust .
Postdoc E: Well PhD G: Cuz we really will get rid of everything that has the " NUMS " string in it .
PhD B: I suppose what you could do is just make sure that you get rid of everything that has " curly brace NUMS curly brace " .
Postdoc E: Well Ex - exactly .
PhD B: I mean that would be the Postdoc E: Exactly . That was that was my motivation . And i these can be changed , like I said .
PhD G: Yeah .
Postdoc E: You know , I mean , as I said I was considering changing it to " digits " . And , it just i you know , it 's just a matter of deciding on whatever it is , and being sure the scripts know .
PhD B: Right .
PhD G: It would probably be safer , if you 're willing , to have a separate tag just because um , then we know for sure . And we can also do counts on them without having to do the processing . But you 're right , we could do it this way , it it should work . Um ,
PhD B: Yeah , and it makes it I guess the thing about PhD G: but it it 's probably not hard for a person to tell the difference
PhD B: Yeah .
PhD G: because one 's in the context of a you know , a transcribed word string ,
PhD B: Right .
Postdoc E: The other thing is you can get really so minute with these things
PhD G: and So Postdoc E: and increase the size of the files and the re and decrease the readability to such an extent by simply something like " percent " . Now I I could have adopted a similar convention for " percent " , but somehow percent is not so hard , you know ?
Grad D: Hmm .
Postdoc E: i It 's just when you have these points and you 're trying to figure out where the decimal places are And we could always add it later . Percent 's easy to detect . Point however is is uh a word that has a couple different meanings . And you 'll find both of those in one of these meetings , where he 's saying " well the first point I wanna make is so - and - so " and he goes through four points , and also has all these decimals .
PhD B: So Liz , what does the recognizer do ,
Postdoc E: So .
PhD B: uh ,
Professor F: Hmm .
PhD B: what does the SRI recognizer output for things like that ? " seven point five " . Does it output the word PhD G: " Seven point five " .
PhD B: Right , the word " seven " ?
Grad D: Well , the numbers ?
PhD B: The number " seven " ?
PhD G: The word .
PhD B: The word " seven " , OK .
Professor F: Yeah .
PhD G: Yeah .
Professor F: So I 'd so " I 'd like I 'd like to talk about point five " .
PhD G: And and actually , you know the language Professor F: Yeah .
PhD G: it 's the same point , actually , the the p you know , the word " to " and the word y th " going to " and " to go to " those are two different " to 's " and so there 's no distinction there .
PhD B: Mm - hmm .
PhD G: It 's just just the word " point " has Yeah , every word has only one , yeah e one version even if even if it 's A actually even like the word " read " {comment} and " read " Those are two different words . They 're spelled the same way , right ?
PhD B: Mm - hmm .
PhD G: And they 're still gonna be transcribed as READ .
PhD B: Mm - hmm .
Professor F: Right .
PhD G: So , yeah , I I like the idea of having this in there , I just I was a little bit worried that , um , the tag for removing the read speech because i What if we have like " read letters " or , I don't know ,
Grad D: We might wanna just a separate tag that says it 's read .
PhD G: like " read something " like " read "
Grad C: Mm - hmm .
PhD G: yeah , basically . But other than that I it sounds great .
Grad D: Yeah . OK ? Are we done ?
Postdoc E: Well I wanted to say also regarding the channelized data ,
Grad D: Oh , I guess we 're not done .
PhD B: Yeah .
Postdoc E: that , um , Thilo requested , um , that we ge get some segments done by hand to e e s reduce the size of the time bins wh like was Chuc - Chuck was mentioning earlier that , um , that , um , if you if you said , " Oh " and it was in part of a really long , s complex , overlapping segment , that the same start and end times would be held for that one
Grad D: Well Postdoc E: as for the longer utterances ,
Grad D: We did that for one meeting , right ,
Postdoc E: and Grad D: so you have that data don't you ?
PhD A: Yeah , that 's the training data .
Postdoc E: And he requested that there be , uh , similar , uh , samples done for five minute stretches c involving a variety of speakers and overlapping secti sections .
PhD A: Yeah .
Postdoc E: He gave me he did the very nice , he he did some shopping through the data and found segments that would be useful . And at this point , all four of the ones that he specified have been done . In addition the I 've I have the transcribers expanding the amount that they 're doing actually .
PhD A: Oh great .
Postdoc E: So right now , um , I know that as of today we got an extra fifteen minutes of that type , and I 'm having them expand the realm on either side of these places where they 've already started .
PhD A: Oh great . OK .
Postdoc E: But if if you know , and I and he 's gonna give me some more sections that that he thinks would be useful for this purpose .
PhD A: Yeah . Yeah .
Postdoc E: Because it 's true , I mean , if we could do the the more fine grained tuning of this , uh , using an algorithm , that would be so much more efficient . And , um . So this is gonna be useful to expand this .
PhD A: So I I thought we we sh we sh perhaps we should try to to start with those channelized versions just to just to try it . Give it Give one tr transcriber the the channelized version of of my speech - nonspeech detection and look if if that 's helpful for them , or just let them try if if that 's better or If they if they can Postdoc E: You mean to start from scratch f in a brand new transcript ?
PhD A: Yeah . Yeah . Yeah .
Postdoc E: That 'd be excellent . Yeah , that 'd be really great . As it stands we 're still in the phase of sort of , um , cleaning up the existing data getting things , uh , in i m more tight tightly time uh , aligned . I also wanna tell um , I also wanted to r raise the issue that OK so , there 's this idea we 're gonna have this master copy of the transcript , it 's gonna be modified by scripts t into these two different functions . And actually the master PhD B: Two or more . Two or more different functions .
Postdoc E: Two two or more . And that the master is gonna be the channelized version .
PhD B: Right .
Postdoc E: So right now we 've taken this i initial one , it was a single channel basically the way it was input . And now , uh , thanks to the advances made in the interface , we can from now on use the channelized part , and , um , any changes that are made get made in the channelized version kind of thing . But I wanted to get all the finished all the checks PhD B: Yeah , so that has implications for your script .
Grad C: Yeah . So , uh , have those e e the vis the ten hours that have been transcribed already , have those been channelized ? And I know I 've seen @ @ I 've seen they 've been channelized ,
Postdoc E: Yes , they have .
Grad D: All ten hours ?
Grad C: but
Postdoc E: Except for the missing thirty minutes .
Grad C: have they uh have they been has the time have the time markings been adjusted , uh , p on a per channel Grad D: Great .
Postdoc E: Uh , for for a total of like twenty m f for a total of Let 's see , four times total of about an  thirty minutes . That 's that 's been the case .
Grad C: So ,
Postdoc E: And plus the training , whatever you have .
Grad C: I guess , I mean , I don't know if we should talk about this now , or not , but I
Grad D: Well it 's just we 're missing tea .