E: Sheep .
C: Yeah
C: he he he sounded to me just from he sounded like a ,
B: Yeah .
C: what 's it a sheep or a goat ?
E: A sheep .
E: Baah .
B: Sheep .
C: Sheep ,
C: right .
B: Yeah .
B: Sheep is good .
C: Sounded good .
B: Yeah .
C: Right so um so I guess the good news is that
G: Mm - hmm .
C: and and again this is without a lot of the sort of bells and whistles that we c can do with the SRI system
C: and we 'll have more data and we can also start to maybe adapt the language models once we have enough meetings .
C: So this is only twenty minutes of one meeting with no no tailoring at all .
A: I mean clearly there are um with just a small amount of uh actual meeting transcriptions uh thrown into the language model you can probably do quite a bit better
C: Yeah .
A: because the E: Or just dictionary .
C: The voca the vocabulary especially
C: yeah .
C: Yeah , so .
A: Not that much the vocabulary actually
A: I think um well we have to see
A: but it 's uh C: Yeah .
C: It 's pretty good
C: um so then
B: Have to add PZM and so on
E: And I have to try it on the far field mike
B: but
E: yeah .
C: PZM
C: and then there 's things like for the transcription I got when someone has a digit in the transcript I don't know if they said , you know one one or eleven
C: and I don't know if they said Tcl or TCL .
C: there 's things like that where , you know the um we 'll probably have to ask the transcribers to indicate some of those kinds of things
C: but in general it was really good
C: and I 'm hoping C: and this is this is good news
C: because that means the force alignments should be good
C: and if the force alignments ,
C: I mean it 's good news anyway
C: but if the force alignments are good we can get all kinds of information .
C: For example about , you know prosodic information
C: and speaker overlaps and so forth directly from the aligned times .
C: Um so that 'll be something that
C: actually in order to assess the forced alignment um we need s some linguists or some people to look at it and say are these boundaries in about the right place .
C: Because it 's just gonna give us time marks
E: Well we 've done that for one meeting .
D: But you know C: so .
C: For forced alignment .
E: Uh oh oh
E: f not for words
C: Ye - right .
E: I 'm sorry just for overlaps is we did it for not not for words .
C: Right .
C: So this would be like if you take the words um you know and force align them on all the individual close talk uh close talking mikes then how good are these sort of in reality
E: Right .
C: and then I was thinking it E: So we might want to take twenty minutes and do a closer word level transcription .
E: Maybe actually mark the word boundaries .
C: Oh
C: or i have someone look at the alignments
C: uh maybe a linguist who can say um you know roughly if these are OK and how far away they are .
B: Yeah .
C: Um but I think it 's gotta be pretty good
C: because otherwise the word recognition would be really b crummy .
E: Right , right .
C: It wouldn't necessarily be the other way around ,
C: if the wor word recognition was crummy the alignment might be OK
C: but if the word recognition is this good the alignment should be pretty good .
C: So that 's about it .
D: I wonder if this is a good thing or a bad thing though ,
B: I r
D: I mean if we 're pr
E: That we 're starting so well ?
D: yeah if we 're producing a database that everybody 's gonna do well on
B: Oh
E: Don't worry about it
E: w d that 's that 's the close talking mikes .
E: Try it on the P Z Ms and and
B: Yeah ,
B: which I would which well n n n n
H: Yeah , yeah , yeah , yeah .
D: So the real value of the database is these ?
E: Yeah , abso
E: well no but
B: I mean there 's still just the w the percentages
B: and , I mean they 're not a
C: This i
B: as we 've talked about before there 's probably overlaps
C: yeah .
C: This is not that good .
B: there 's probably overlaps in in uh in fair number in Switchboard as well
B: so
B: but but there 's other phenomena ,
B: it 's a meeting ,
B: it 's a different thing and there 's lots of stuff to learn with the close talking mikes
B: but uh
B: yeah certainly I 'd like to see as soon as we could ,
B: I mean maybe get some of the glitches out of the way
B: but soon as we could how well it does with say with the P Z Ms or maybe even one of the
C: Right .
B: and uh see if it 's , you know
B: is it a hundred twenty percent
B: or maybe it 's not
B: maybe if with some adaptation you get this down to fifty percent or forty - five percent or something
B: and and then if for the PZM it 's seventy or something like that
B: that 's actually something we could sort of work with a little bit
C: Yeah .
C: No I think it 's really ,
B: so
C: I mean this way we least have a baseline
C: we know that for instance the transcripts are very good
C: so once you can get to the words that the recognizer which is a total subset of the things you need to understand the the text
C: um yeah they 're pretty good
C: so and and it 's converting automatically from the XML to the chopping up the wave forms and so forth
C: it 's not the case that the end of one utterance is in the next segment and things like that which we had more problems with in Switchboard
C: so that 's good .
C: And um let 's see
C: there was one more thing I wanted to to mention C: I can't remember
C: um
C: Sorry can't remember .
C: anyway it 's G: Congratulations
G: is really great .
C: well it was , I mean I really didn't do this myself
B: Yeah .
E: Yeah , it 's really good .
C: so Andreas set up this recognizer and C: by the way the recognizer all the files I 'm moving to SRI and running everything there
C: so I brought back just these result files
C: and people can look at them
C: um so
A: We we talked about setting up the SRI recognizer here .
A: That 's you know if if there are more machines um uh here
A: plus people can could run their own uh you know variants of of of the recognition runs
A: um
A: certainly doable .
A: Um .
B: Yeah and B: well certainly if the recognition as opposed to training ,
B: yeah .
B: Seems reasonable .
G: I need t Hmm . I need to ask one question .
A: Yeah .
A: Yeah .
G: Which is um so this issue of the uh legalistic aspects of the pre - sent you know pre - adapted G: Yeah , well , so what I mean is
G: um
G: the uh the data that you take into SRI , first first question ,
G: you 're maintaining it in in a place that wouldn't be publicly readable that that kind of stuff ,
G: right ?
A: U um
C: From the outside world or
G: By uh people uh who are not associated with this project .
E: It 's human subjects issues ,
A: Oh .
E: I told you about that .
G: Exactly .
C: Um oh .
C: Well OK
C: we have n no names .
C: Although I sh um
E: That that 's not the issue ,
E: it 's just the audio data itself ,
C: de audio data itself ?
E: until people have a chance to edit it .
G: Mm - hmm , exactly .
C: Uh so well I can I can protect my directories through there .
G: Yeah .
G: Great .
C: Right now they 're not they 're in the speech group directories which so I will C: I didn't know that actually .
B: Yeah so we just have to go through this process of having people approve the transcriptions ,
C: Yeah OK .
B: say it 's OK .
G: Yeah , we had to get them to approve em
C: Right OK .
G: and then i cuz cuz the other question I was gonna ask is if we 're having um you know it 's
G: but this this meeting that you have , no problem
G: cuz I I well I mean I I speak for myself
E: It 's us .
G: but but I think that we didn't do anything that but
G: well anyway so uh I wouldn't be too concerned about it with respect to that
G: although we should clear it with Eric and Dan of course
G: but these results are based on data which haven't had the uh haven't had the chance to be reviewed by the subjects
C: That 's true .
G: and I don't know how that stands ,
G: I mean if you if you get fantastic results and it 's involving <comment> data which which later end up being lessened by , you know certain elisions , then I don't know
G: but I wanted to raise that issue ,
G: that 's all .
B: Well we ,
B: I mean once we get all this streamlined it may be sh it B: hopefully it will be fairly quick
B: but we get the transcriptions , people approve them and so on
E: Alright
B: it 's just that we 're
E: we need to work at a system for doing that approval so that we can send people the transcripts
G: Great .
A: Mmm .
G: Yeah .
E: and get back any bleeps that they want
C: Yeah actually the bleeps are also an issue I thought .
B: It 's gonna be a rare thing that there 's a bleep for the most part .
A: U uh actually I had a question about the downsampling ,
A: um I don't know who , I mean how this was done
A: but is is there are there any um issues with downsampling
C: Don did this .
A: because I know that the recognizer um that we use h can do it sort of on the fly
A: um
A: so we wouldn't have to have it eh you know do it uh explicitly beforehand .
A: And is there any um i
A: are there other d sev uh is there more than one way to do the downsampling where one might be better than another ?
F: There are lots of w there are lots of ways to do the downsampling um different filters to put on ,
A: OK .
A: Right .
F: like anti - aliasing stuff .
A: OK .
A: So so the th
E: I don't think we even know which one
E: I assume you 're using syncat to do it ?
F: No , I 'm using uh SN SND uh are resample .
E: Or sound resample ?
C: Re - re ref
E: Resample .
C: yeah .
E: Yeah and Dan 's archaic acronyms .
F: RSMP .
F: Yeah , I don't really .
C: Missing all the vowels .
F: I just yeah I found it .
E: Not all of them .
C: Some of the vowels ,
C: almost all the vowels ,
C: that 's the hard part .
A: So so the other thing we should try is to just take the original wave forms ,
E: And a few of the consonants .
A: I mean segment them but not downsample them .
C: Yeah we could we could try that and and compare