E: I guess .
A: OK , we 're on .
A: So just make sure that th your wireless mike is on , if you 're wearing a wireless .
E: Check one .
E: Check one .
A: And you should be able to see which one which one you 're on by , uh , watching the little bars change .
B: So , which is my bar ?
B: Mah !
B: Number one .
A: Yep .
E: Sibilance .
E: Sibilance .
A: So , actually , if you guys wanna go ahead and read digits now , as long as you 've signed the consent form , that 's alright .
E: Are we supposed to read digits at the same time ?
A: No .
E: Oh , OK .
A: No .
A: Each individually .
A: We 're talking about doing all at the same time
A: but I think cognitively that would be really difficult . To try to read them while everyone else is .
E: Everyone would need extreme focus .
A: So , when you 're reading the digit strings , the first thing to do is just say which transcript you 're on .
C: Other way .
C: We m We may wind up with ver We we may need versions of all this garbage .
B: For our stuff .
C: Yeah .
B: Yeah .
A: Um . So the first thing you 'd wanna do is just say which transcript you 're on .
C: Yeah .
A: So .
A: You can see the transcript ?
A: There 's two large number strings on the digits ?
A: So you would just read that one .
A: And then you read each line with a small pause between the lines .
A: And the pause is just so the person transcribing it can tell where one line ends and the other begins .
A: And I 'll give I 'll read the digit strings first , so can see how that goes .
A: Um .
A: Again , I 'm not sure how much I should talk about stuff before everyone 's here .
C: Mmm .
C: Well , we have one more coming .
A: OK .
A: Well , why don't I go ahead and read digit strings
A: and then we can go on from there .
C: OK .
C: Well , we can start doing it .
A: Thanks .
A: So , uh , just also a note on wearing the microphones .
A: All of you look like you 're doing it reasonably correctly ,
A: but you want it about two thumb widths away from your mouth ,
A: and then , at the corner .
A: And that 's so that you minimize breath sounds ,
A: so that when you 're breathing , you don't breathe into the mike .
A: Um .
A: Yeah , that 's good .
A: And uh A: So , everyone needs to fill out , only once , the speaker form and the consent form .
A: And the short form A: I mean , you should read the consent form ,
A: but uh , the thing to notice is that we will give you an opportunity to edit a all the transcripts .
A: So , if you say things and you don't want them to be released to the general public , which , these will be available at some point to anyone who wants them , uh , you 'll be given an opportunity by email , uh , to bleep out any portions you don't like .
A: Um .
A: On the speaker form just fill out as much of the information as you can .
A: If you 're not exactly sure about the region ,
A: we 're not exactly sure either .
A: So , don't worry too much about it .
A: The It 's just self rating .
A: Um .
A: And I think that 's about it .
A: I mean , should I Do you want me to talk at all about why we 're doing this and what this project is ?
C: Um , yeah .
A: or ?
C: No . There was there was Let 's see .
E: Does Nancy know that we 're meeting in here ?
C: Oh C: She got an emai she was notified .
B: I sent an email .
E: Oh yeah , she got an e
E: Yeah , yeah .
C: Whether she knows is another question .
C: Um .
C: So are the people going to be identified by name ?
A: Well , what we 're gonna we 'll anonymize it in the transcript .
C: Right .
A: Um , but not in the audio .
C: OK .
C: So , then in terms of people worrying about , uh , excising things from the transcript , it 's unlikely .
A: So the
C: Since it it does isn't attributed .
C: Oh , I see ,
C: but the a but the but the A: Right , so if I said , " Oh , hi Jerry , how are you ? " , we 're not gonna go through and cancel out the " Jerry "s .
C: Yeah .
C: Sure .
A: Um , so we will go through and , in the speaker ID tags there 'll be , you know , M - one O seven , M - one O eight .
C: Right .
C: Right .
A: Um , but uh ,
A: um , it w uh ,
A: I don't know a good way of doing it on the audio , and still have people who are doing discourse research be able to use the data .
C: OK .
C: Mm - hmm .
C: No , I I wasn't complaining ,
C: I just wanted to understand .
A: Yep .
A: Right .
C: OK .
B: Well , we can make up aliases for each of us .
A: Yeah , I mean , whatever you wanna do is fine ,
C: Right .
F: OK .
A: but we find that A: We want the meeting to be as natural as possible .
C: OK .
A: So , we 're trying to do real meetings .
A: And so we don't wanna have to do aliases
C: Right .
A: and we don't want people to be editing what they say .
B: Right .
C: Right .
A: So I think that it 's better just as a pro post - process to edit out every time you bash Microsoft .
B: Mm - hmm .
A: You know ?
C: Right .
C: Um , OK . So why don't you tell us briefly
C: your give give your e normal schpiel .
A: OK .
A: So th
A: Um . So this is The project is called Meeting Recorder
A: and there are lots of different aspects of the project .
A: Um . So my particular interest is in the PDA of the future .
A: This is a mock - up of one .
A: Yes , we do believe the PDA of the future will be made of wood .
A: Um . <comment> The idea is that you 'd be able to put a PDA at the table at an impromptu meeting ,
A: and record it ,
A: and then be able to do querying and retrieval later on , on the meeting .
A: So that 's my particular interest ,
A: is a portable device to do m uh , information retrieval on meetings .
A: Other people are interested in other aspects of meetings .
A: Um . So the first step on that , in any of these , is to collect some data .
A: And so what we wanted is a room that 's instrumented with both the table top microphones ,
A: and these are very high quality pressure zone mikes ,
A: as well as the close talking mikes .
A: What the close talk ng talking mikes gives us is some ground truth ,
A: gives us , um , high quality audio ,
A: um , especially for people who aren't interested in the acoustic parts of this corpus .
A: So , for people who are more interested in language , we didn't want to penalize them by having only the far field mikes available .
A: And then also , um , it 's a very , very hard task in terms of speech recognition .
A: Um . And so , uh , on the far field mikes we can expect very low recognition results .
A: So we wanted the near field mikes to at least isolate the difference between the two .
A: So that 's why we 're recording in parallel with the close talking and the far field at the same time .
A: And then , all these channels are recorded simultaneously and framed synchronously
A: so that you can also do things like , um , beam - forming on all the microphones
A: and do research like that .
A: Our intention is to release this data to the public , um , probably through f through a body like the LDC .
A: And , uh , just make it as a generally available corpus .
A: Um . There 's other work going on in meeting recording .
A: So , we 're we 're working with SRI ,
A: with UW ,
A: Um . NIST has started an effort which will include video .
A: We 're not including video , obviously .
A: And uh and then also , um , a small amount of assistance from IBM .
A: Is also involved .
A: Um . Oh , and the digit strings , this is just a more constrained task .
A: Um . So because the general environment is so challenging , we decided to to do at least one set of digit strings
A: to give ourselves something easier .
A: And it 's exactly the same digit strings as in TI - digits ,
A: which is a common connected digits corpus .
A: So we 'll have some , um , comparison to be able to be made .
C: OK .
A: Anything else ?
C: No .
A: OK , so when the l last person comes in , just have them wear a wireless .
A: It should be on already .
A: Um .
A: Either one of those .
A: And uh , read the digit strings and and fill out the forms .
A: So , the most important form is the consent form ,
A: so just be s be sure everyone signs that , if they consent .
B: I 'm sure it 's pretty usual for meetings that people come late ,
B: so you will have to leave what you set .
A: Yeah .
A: Right .
A: And uh , just give me a call ,
A: which , my number 's up there when your meeting is over .
C: Yep .
A: And I 'm going to leave the mike here
A: but it 's n <nonvocalsound> Uh , but I 'm not gonna be on
A: so don't have them use this one .
A: It 'll just be sitting here .
B: Input ?
B: Yeah .
B: There we go .
C: By the way , Adam , we will be using the , uh , screen as well .
B: Yep .
C: So , you know .
C: Wow !
C: Organization .
C: So you guys who got email about this oh f uh , Friday or something about what we 're up to .
E: No .
F: No .
B: I got it .
E: What was the nature of the email ?
C: Oh , this was about um , inferring intentions from features in context , and the words ,
C: like " s go to see " , or " visit " , or some
B: Wel - we I uh I I C: You didn't get it ?
E: I don't think I did .
C: I guess these g have got better filters .
C: Cuz I sent it to everybody .
C: You just blew it off .
E: Ah .
C: OK .
B: It 's really simple though .
B: So this is the idea .
B: Um .
B: We could pursue , um , if we thought it 's it 's worth it but , uh , I think we we will agree on that , um , to come up with a with a sort of very , very first crude prototype ,
B: and do some implementation work , and do some some research , and some modeling .
B: So the idea is if you want to go somewhere ,
B: um , and
B: focus on that object down B: Oh , I can actually walk with this .
B: This is nice .
B: down here .
B: That 's the Powder - Tower .
B: Now , um , we found in our , uh , data and from experiments , that there 's three things you can do .
B: Um , you can walk this way , and come really , really close to it .
B: And touch it .
B: But you cannot enter or do anything else .
B: Unless you 're interested in rock climbing , it won't do you no good standing there .
B: It 's just a dark alley .
B: But you can touch it .
B: If you want to actually go up or into the tower , you have to go this way ,
B: and then through some buildings and up some stairs and so forth .
B: If you actually want to see the tower , and that 's what actually most people want to do , is just have a good look of it , take a picture for the family , <comment> you have to go this way , and go up here .
B: And there you have a vre really view It exploded , the during the Thirty
B: years - war . Really uh , interesting sight .
E: Mmm .
B: And um , these uh these lines are , um , paths ,
B: or so That 's ab er , i
B: the street network of our geographic information system .
B: And you can tell that we deliberately cut out this part .
B: Because otherwise we couldn't get our GIS system to take to lead people this way .
B: It would always use the closest point to the object ,
B: and then the tourists would be faced , you know , in front of a wall ,
B: but it would do them absolutely no good .
B: So , what we found interesting is , first of all , intentions differ .
B: Maybe you want to enter a building .
B: Maybe you want to see it ,
B: take a picture of it .
B: Or maybe you actually want to come as close as possible to the building .
B: For whatever reason that may be .
E: What 's it what 's it made out of ?
B: Um , r red limestone .
E: So maybe you would wanna touch it .
B: Yeah , maybe you would want to touch it .
B: Um .
B: Okay , I This , um These intentions , we w w we could , if we want to , call it the the Vista mode ,
B: where we just want to eh s get the overview or look at it ,
B: the Enter mode ,
B: and the , well , Tango mode .
B: I always come up with with silly names .
B: So this " Tango " means , literally translated , " to touch " .
B: So But sometimes the the Tango mode is really relevant in the in the sense that , um , if you want to , uh B: If you don't have the intention of entering your building , but you know that something is really close to it , and you just want to approach it , or get to that building .
B: Consider , for example , the Post Office in Chicago , a building so large that it has its own zip code .
B: So the entrance could be miles away from the closest point .
B: So sometimes it m m m makes sense maybe to d to distinguish there .
B: So , um , I 've looked , uh , through twenty some B: Uh , I didn't look through all the data .
B: um , and there there 's uh , a lot more different ways in people uh , the ways people phrase how to g get if they want to get to a certain place .
B: And sometimes here it 's b it 's a little bit more obvious B: Um . Maybe I should go back a couple of steps
B: and go through the C: No , OK come in , sit down .
C: If you grab yourself a microphone .
B: You need to sign some stuff and read some digits .
C: Well , you can sign afterwards .
B: O or later .
E: You have to al also have to read some digits .
C: Afterwards .
D: OK . <comment> OK . Afterwards is fine .
B: They are uncomfortable .
B: Mm - hmm .
D: Really small ?
D: OK . I see .
D: OK .
B: Yep .
D: Thank you .
B: OK , but that was our idea .
C: And it it it it it also has to be switched on , Nance .
B: Is E: No , that one 's already on , I thought he said .
B: I I think C: It 's on ?
C: OK , good .
E: Yeah .
D: OK . It 's on .
B: OK . That was the idea .
B: Um , people , when they w when they want to go to a building , sometimes they just want to look at it .
B: Sometimes they want to enter it .
B: And sometimes they want to get really close to it .
B: That 's something we found .
B: It 's just a truism .
B: And the places where you will lead them for these intentions are sometimes ex in incredibly different .
B: I I gave an example where the point where you end up if you want to look at it is completely different from where if you want to enter it .
B: So , this is sort of how people may , uh may phrase those requests to a a a mock - up system at least that 's the way they did it .
B: And we get tons of of these " how do I get to " , " I want to go to " ,
B: but also , " give me directions to " , and " I would like to see " .
B: And um , what we can sort of do , if we look closer a closer at the the data B: That was the wrong one .
B: um , we can look at some factors that may make a difference .
B: First of all ,
B: very important ,
B: and um , that I 've completely forgot that when we talked .
B: This is of course a crucial factor , " what type of object is it ? "
B: So , some buildings you just don't want to take pictures of .
B: Or very rarely .
B: But you usually want to enter them .
B: Some objects are more picturesque ,
B: and you more f more highly photographed .
B: Then of course the the actual phrases may give us some idea of what the person wants .
B: Um . Sometimes I found in the Uh , looking at the data , in a superficial way , I found some s sort of modifiers that that m may also give us a hint ,
B: um , " I 'm trying to get to "
B: Nuh ?
B: " I need to get to " .
B: Sort of hints to the fact that you 're not really sightseeing and and just f there for pleasure and so forth and so on .
B: And this leads us straight to the context which also should be considered .
B: That whatever it is you 're doing at the moment may also inter influence the interpretation of of a phrase .
B: So , this is , uh , really
B: uh , uh , uh B: My suggestion is really simple .
B: We start with , um B: Now , Let me , uh , say one more thing .
B: What we do know , is that the parser we use in the SmartKom system will never differentiate between any of these .
B: So , basically all of these things will result in the same XML M - three - L structure .
B: Sort of action " go " ,
B: and then an object .
D: Mm - hmm .
B: Yeah ?
B: and a source .
B: So it 's it 's it 's way too crude to d capture those differences in intentions .
B: So , I thought , " Mmm !
B: Maybe for a deep understanding task , that 's a nice sort of playground or first little thing . "
B: Where we can start it and n sort of look B: " OK , we need , we gonna get those M - three - L structures .
B: The crude , undifferentiated parse .
B: Interpreted input .
B: We may need additional part of speech , or maybe just some information on the verb , and modifiers , auxiliaries .
B: We 'll see .
B: And I will try to to sort of come up with a list of factors that we need to get out of there ,
B: and maybe we want to get a g switch for the context .
B: So this is not something which we can actually monitor , now ,
B: but just is something we can set .
B: And then you can all imagine sort of a a constrained satisfaction program , depending on on what , um , comes out .
B: We want to have an a structure resulting
B: if we feed it through a belief - net or or something along those lines . We 'd get an inferred intention , we we produce a structure that differentiates between the Vista , the Enter , and the , um , Tango mode .
B: Which I think we maybe want to ignore .
B: But . That 's my idea .
B: It 's up for discussion .
B: We can change all of it ,
B: any bit of it .
B: Throw it all away .
F: Now @ @ this email that you sent , actually .
C: What ?
F: Now I remember the email .
C: OK .
E: Huh .
E: Still , I have no recollection whatsoever of the email .
E: I 'll have to go back and check .
C: Not important .
C: So , what is important is that we understand what the proposed task is .
C: And , the the i uh , Robert and I talked about this some on Friday .
C: And we think it 's well - formed .
C: So we think it 's a well - formed , uh , starter task for this , uh , deeper understanding in the tourist domain .
F: So , where exactly is the , uh , deeper understanding being done ?
F: Like I mean , s is it before the Bayes - net ?
F: Is it , uh C: Well , it 's the it 's it 's always all of it .
C: So , in general it 's always going to be , the answer is , everywhere .
C: Uh , so the notion is that , uh , this isn't real deep .
C: But it 's deep enough that you can distinguish between these th three quite different kinds of , uh , going to see some tourist thing .
C: And , so that 's that 's the quote " deep " that we 're trying to get at .
C: And , Robert 's point is that the current front - end doesn't give you any way to C: Not only doesn't it do it , but it also doesn't give you enough information to do it .
C: It isn't like , if you just took what the front - end gives you , and used some clever inference algorithm on it , you would be able to figure out which of these is going on .
C: So , uh ,
C: and this is Bu - I in general it 's gonna be true of any kind of deep understanding ,
C: there 's gonna be contextual things ,
C: there 're gonna be linguistic things ,
C: there 're gonna be discourse things ,
C: and they gotta be combined .
C: And , my idea on how to combine them is with a belief - net ,
C: although it may turn out that t some totally different thing is gonna work better .
C: Um , the idea would be that you , uh , take your C: You 're editing your slide ?
B: Yeah . As i a sort of , as I get ideas ,
B: uh w uh .
C: Oh .
B: So , discourse I I I thought about that .
B: Of course that needs to sort of go in there .
C: Oh . I 'm sorry .
C: OK . So . This is minutes taking minutes as we go ,
C: in his in his own way .
B: Yep .
C: Um , but the p the C: Anyway . So the thing is , i uh , d naively speaking , you 've you 've got a for this little task , a belief - net ,
C: which is going to have as output , the conditional pr probability of one of three things ,
C: that the person wants to uh , to View it , to Enter it , or to Tango with it .
C: Um .
C: So that the the output of the belief - net is pretty well formed .
C: And , then the inputs are going to be these kinds of things .
C: And , then the question is C: there are two questions C: is , uh , one , where do you get this i <comment> information from ,
C: and two , what 's the structure of the belief - net ?
C: So what are the conditional probabilities of this , that , and the other , given these things ?
C: And you probably need intermediate nodes .
C: I we don't know what they are yet .
C: So it may well be that , uh , for example , that , uh , knowing whether C: Oh , another thing you want is some information abou I think , about the time of day .
C: Now , they may wanna call that part of context .
C: But the time of day matters a lot .
B: Mm - hmm .
C: And , if things are obviously closed , then , you