grad e: OK , so for for For people wearing the wireless mikes , like like this one , I find the easiest way to wear it is sorta this this sorta like that . phd h: This is chan channel channel one one two three grad f: Channel five , channel five . What do you do , grad e: It 's actually a lot more comfortable then if you try to put it over your temples , grad f: Test , test test . grad e: And then also , for for all of them , if your boom is adjustable , the boom should be towards the corner of your mouth , grad f: Test test . grad e: and about a  a thumb to a thumb and a half distance away from your mouth , phd a: basically it wasn't adapting anything . So why didn't you get the same results and the unadapted ? grad e: so so Jane , you could actually do even a little closer to your mouth , phd h: It 's not always possible . phd a:  ? phd d: Why didn't you get the same results and the unadapted ? postdoc g: I could can this be adjuste like this ? grad e: but phd a: Oh , because when it estimates the transformer pro produces like a single matrix or something . grad f: Hello ? grad e: we could we could up the gain slightly if you wanted to . phd c: Who 's channel B ? grad e: but  , channel B is probably Liz . phd c: So can you talk a bit ? I thought it might be too phd h: OK , yeah , channel B , one two three four five . grad e:  everyone should have at least two forms possibly three in front of you depending on who you are . grad e:  we we 're doing a new speaker form and you only have to spea fill out the speaker form once but everyone does need to do it . grad e: We we had a lot of discussion about the variety of English and so on so if you don't know what to put just leave it blank .  I I designed the form and I don't know what to put for my own region , phd a: Mmm . phd a:  may I make one suggestion ? Instead of age put date of  year of birth grad e: Sure . phd a: because age will change , but The year of birth changes , you know , stays the same , usually . phd c: A actually , wait a minute , grad e: Birth year ? postdoc g: Although on phd a: Yeah . postdoc g: course on the other on the other hand you could you view it as the age at the time of the phd c: On the other side , phd a: Well the thing is , if ten years from now you look at this form knowing that phd c: yeah . postdoc g: Yes , but what we care about is the age at at the recording date rather than the phd c: O yeah . grad e: and then  there will be attached to this a point or two these forms  so that you 'll be able to extract the date off that phd a:   And so then you also have a digits form which needs to be filled out every time , the speaker form only once , the digit form every time even if you don't read the digits you have to fill out the digits form so that we know that you were at the meeting . OK ? And then also if you haven't filled one out already you do have to fill out a consent form . professor b: So  grad e: OK so should we do agenda items ? professor b:  oh that 's a good idea . Anything else ? phd c: I guess what time do we have to leave ? Three thirty ? phd a: Yeah . phd c:  Well , I I sent out an email s couple hours ago so  with Andreas ' help  Andreas put together a sort of no frills recognizer which is  gender - dependent but like no adaptation , no cross - word models , no trigrams a bigram recognizer and that 's trained on Switchboard which is telephone conversations .  and thanks to Don 's help wh who Don took the first meeting that Jane had transcribed and  you know separated used the individual channels we segmented it in into the segments that Jane had used and  Don sampled that so so eight K  and then we ran up to I guess the first twenty minutes , up to synch time of one two zero zero so is that that 's twenty minutes or so ?  yeah because I guess there 's some , grad e: Or so . phd c: and Don can talk to Jane about this , there 's some bug in the actual synch time file that ah  I 'm we 're not sure where it came from but stuff after that was a little messier . phd c:  grad e: I was that did that did that recording have the glitch in the middle ? postdoc g: I 'm puzzled by that . phd c: There 's there 's a postdoc g: Oh there was a glitch somewhere . phd c: yeah , so that actually  grad f: Was it twenty minutes in , phd c: if it was twenty minutes in then I don't know postdoc g: I forgot about that . grad f: I thought phd a: Well it was interesting , postdoc g: Well , they phd a: suddenly the the overall error rate when we first ran it was like eighty percent grad e: I don't remember when it is . postdoc g: but I was able to can transcribe phd a: but i looking at the first sentences looked much better than that and then suddenly it turned very bad and then we noticed that the reference was always one off with the it was actually recognized phd c: Wel grad e: Oh no . phd c: No actually it was yeah i it was a complicated bug because they were sometimes one off and then sometimes totally random so  grad f: yeah , I was pretty certain that it worked up until that time , postdoc g: Oh . grad f: so phd c: but that that will be completely gone if this synch time problem postdoc g: Yeah . grad e: The the glitch phd a: So so we have everything recognized but we scored only the first  whatever , up to that time to postdoc g: And the only glitch grad e: yeah . grad e: Th - the postdoc g: The the well wait professor b: what was the score ? phd c: So here 's the actual copy of the email postdoc g: we should say something about the glitch . postdoc g: Cuz it 's it 's it 's h it 's it 's very small phd c: so does this glitch occur at other grad e: There there there 's an acoustic glitch that occurs where  the channels get slightly asynchronized postdoc g: very small . grad e: so the that that problem has gone away in the original driver believe it or not when the SSH key gen ran the driver paused for a fraction of a second professor b:  . grad e: and so the channels get a little asynchronous and so if you listen to it in the middle there 's a little part where it starts doing doing click sounds . professor b: So phd c: And is it only once that that happens ? grad e: But yeah phd c: OK . phd c: There 's the previous page has some more information about sort of what was wrong professor b: so so un unsurprisingly Adam is the golden voice , phd c: but grad e:  But that shouldn't affect anything phd c: OK so that 's actually postdoc g: S and it professor b: you see this here ? phd c: It y it 's grad e: yeah yeah " bah " phd c: OK no phd a: Oh , and phd c: What happens is it actually affects the script that Don phd d: Huh . phd c:  if we know about it then I guess it could always be checked for it grad e: Well the acoustic one shouldn't do anything . phd c: but they grad f: Yeah , I don't know exactly what affected it postdoc g: I agree . phd a: I I have grad f: but I 'll I 'll talk to you about it , phd a: Yeah . postdoc g: you know ,  I I had no trouble hearing it and and having time bins grad e: I do remember seeing once the transcriber produce an incorrect XML file where one of the synch numbers was incorrect . phd c: Well , the the synch time the synch numbers have more significant digits than they should , grad f: That 's what happened . grad f: There was yeah ,  phd c: right ? There 's things that are l in smaller increments than a frame . phd c: And so then ,  you look at that and it 's got you know more than three significant digits in a synch time then that can't be right grad e: Oh OK so that 's grad f:  . phd c: so anyway it 's it 's just grad e: yeah sounds like a bug . phd c: that 's why we only have twenty minutes but there 's a significant amount of grad f: Non - zero ?  there are like more cuz there 's a lot of zeros I tacked on just because of the way the script ran , grad e: The other one I saw was that it yeah . grad e: The other one I saw was non non - monotonic synch times grad f: OK . phd c: there 's no  the language model for Switchboard is totally different so you can see some like this Trent Lott which phd d: Trent Lott . phd c: there 's a lot of perfect ones and good ones and all the references ,  you can read them and when we get more results you can look through and see grad e: I and as I said I would like to look at the lattices phd a:   grad e: because it sounded like even the ones it got wrong it sort of got it right ? phd c: Well so I guess we can generate grad e: Sounds likes ? phd a: There are a fair number of errors that are , you know where got the plural S wrong or the inflection on the verb wrong . phd c:  grad e: Yeah , and who cares ? And and there were lots of of course the "   " - s , " in on " - s " of  " - s . phd a: Mmm , so if phd c: there 's No those are actually phd a: Yeah . phd c: so is it like PZM is three words , it 's PZM , phd a:   phd c:  there 's nothing There 's no language model for PZM or grad e: Right . phd c:  grad e: Did you say there 's no language for PZM ? phd c: No language model ,  those grad e: Do you mean so every time someone says PZM it 's an error ? Maybe we shouldn't say PZM in these meetings . phd c: Well well there 's all kinds of other stuff like Jimlet and   anyway there grad e: Yeah , that 's right , Jimlet . professor b: Well , we don't even know what that means , phd c: so but this is really encouraging because professor b: so I grad e: Yeah , that 's right . phd c: so ,  the bottom line is even though it 's not a huge amount of data  it should be  reasonable to actually run recognition and be like within the scope of of r reasonable s you know Switchboard this is like h about how well we do on Switchboard - two data with the Switchboard - one trained mostly trained recognizer grad e: Right . phd c: and Switchboard - two is got sort of a different population of speakers and a different topic grad e: Excellent . phd c: and they 're talking about things in the news that happened after Switchboard - one so there was @ @ so that 's great . Yeah so we 're in better shape than we were say when we did had the ninety - three workshop phd c:  professor b: and we were all getting like seventy percent error on Switchboard . phd c: Oh yeah professor b: you know phd c:  this is really , phd a: Mmm . grad e: Well especially for the very first run ,  you phd a: Oh it 's the professor b: Yeah . phd c: yeah grad e: the first run I ran of Switchboard I got a hundred twenty percent word error but phd c: So and what al also this means is that postdoc g: Right . phd c:  grad e: Not Switchboard , phd a: Well it 's phd c:  there 's a bunch of things in this note to various people grad e:  Broadcast News . phd c: especially I guess  with Jane that that would help for since we have this new data now  in order to go from the transcripts more easily to  just the words that the recognizer would use for scoring . I had to deal with some of it by hand but I think a lot of it can be automated s by professor b: Oh one thing I guess I didn't get so you know the language model was straight from from bigram from Switchboard the acoustic models were also from Switchboard or or phd a: Yeah . professor b: So they didn't have anything from this acoustic data in yet ? postdoc g: That 's amazing . phd c: And actually we actually  used Switchboard telephone bandwidth models postdoc g: That 's amazing . phd a: Well that 's those are the only we ones there are , professor b: Yeah . phd c: which I guess phd d: I was just gonna say , phd c: so that 's the on that 's the only acoustic training data that we have a lot of phd d: yeah . phd c: and I guess Ramana , so a guy at SRI said that  there 's not a huge amount of difference going from professor b: Right . phd c: it 's it 's not like we probably lose a huge amount but we won't know because we don't have any full band models for s conversational speech . phd d: It 's probably not as bad as going f using full band models on telephone band speech phd c: So . professor b: Yeah , phd c: Right , so it 's so professor b: but for Broadcast News when we we played around between the two there wasn't a huge loss . phd c: Yeah phd a: I should I should say that the language model is not just Switchboard phd c: so I wou so that 's good . phd a: it 's also  there 's  actually more data is from Broadcast News but with a little less weight phd c: Yeah . phd c: I guess Switchboard was before phd a:  By the way just for fun we also ran , phd c:  . phd a: so just for the heck of it I ran that grad e: And it said a hundred percent male ? phd a:  and it might be reassuring for everybody to know that it got all the genders right . phd c: The j phd a: Yeah so grad e: Oh it did ? postdoc g: Oh that 's I 'm glad . grad e: It got all two genders ? phd c: Yeah but you know Jane and Adam have you kn about equal performance phd a: Yeah . phd c: and  and that 's interesting cuz I think the their language models are quite different so and I I 'm pretty sure from listening to Eric that , you know given the words he was saying and given his pronunciation that the reason that he 's so much worse is the lapel . postdoc g: That makes a lot of sense , phd c: So it 's nice now if we can just sort of eliminate the lapel one when when we get new microphones postdoc g: yeah . professor b: Yeah I I I would bet on that too phd c: that would be worth it professor b: cuz he certainly in that when as a as a burp user he was he was a pretty  strong one . phd c: he he he sounded to me just from he sounded like a , professor b: Yeah . phd 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 and we 'll have more data and we can also start to maybe adapt the language models once we have enough meetings . phd a:  clearly there are  with just a small amount of  actual meeting transcriptions  thrown into the language model you can probably do quite a bit better because the phd c: Yeah . It 's pretty good  so then professor b: Have to add PZM and so on grad e: And I have to try it on the far field mike professor b: but phd c: PZM grad e: yeah . phd 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 and I don't know if they said Tcl or TCL . there 's things like that where , you know the  we 'll probably have to ask the transcribers to indicate some of those kinds of things but in general it was really good and I 'm hoping and this is this is good news because that means the force alignments should be good and if the force alignments ,  it 's good news anyway but if the force alignments are good we can get all kinds of information . For example about , you know prosodic information and speaker overlaps and so forth directly from the aligned times .  so that 'll be something that actually in order to assess the forced alignment  we need s some linguists or some people to look at it and say are these boundaries in about the right place . Because it 's just gonna give us time marks phd d: But you know grad e: Well we 've done that for one meeting . grad e: I 'm sorry just for overlaps is we did it for not not for words . So this would be like if you take the words  you know and force align them on all the individual close talk  close talking mikes then how good are these sort of in reality grad e: Right . phd c: and then I was thinking it grad e: So we might want to take twenty minutes and do a closer word level transcription . phd c: Oh or i have someone look at the alignments  maybe a linguist who can say  you know roughly if these are OK and how far away they are . phd c:  but I think it 's gotta be pretty good because otherwise the word recognition would be really b crummy . phd c: It wouldn't necessarily be the other way around , if the wor word recognition was crummy the alignment might be OK but if the word recognition is this good the alignment should be pretty good . professor b: I r phd d: I wonder if this is a good thing or a bad thing though ,  if we 're pr grad e: That we 're starting so well ? phd d: yeah if we 're producing a database that everybody 's gonna do well on professor b: Oh grad e: Don't worry about it w d that 's that 's the close talking mikes . Try it on the P Z Ms and and professor b: Yeah , which I would which well n n n n phd d: So the real value of the database is these ? phd h: Yeah , yeah , yeah , yeah . grad e: Yeah , abso well no but professor b:  there 's still just the w the percentages and ,  they 're not a as we 've talked about before there 's probably overlaps phd c: This i yeah . professor b: there 's probably overlaps in in  in fair number in Switchboard as well so but but there 's other phenomena , it 's a meeting , it 's a different thing and there 's lots of stuff to learn with the close talking mikes but  yeah certainly I 'd like to see as soon as we could ,  maybe get some of the glitches out of the way but soon as we could how well it does with say with the P Z Ms or maybe even one of the phd c: Right . professor b: and  see if it 's , you know is it a hundred twenty percent or maybe it 's not maybe if with some adaptation you get this down to fifty percent or forty - five percent or something and and then if for the PZM it 's seventy or something like that that 's actually something we could sort of work with a little bit phd c: Yeah . professor b: so phd c: No I think it 's really ,  this way we least have a baseline we know that for instance the transcripts are very good 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  yeah they 're pretty good so and and it 's converting automatically from the XML to the chopping up the wave forms and so forth 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 so that 's good . And  let 's see there was one more thing I wanted to to mention I can't remember  Sorry can't remember . phd c: well it was ,  I really didn't do this myself grad e: Yeah , it 's really good . phd c: so Andreas set up this recognizer and by the way the recognizer all the files I 'm moving to SRI and running everything there so I brought back just these result files and people can look at them  so phd a: We we talked about setting up the SRI recognizer here . That 's you know if if there are more machines   here plus people can could run their own  you know variants of of of the recognition runs  certainly doable . postdoc g: Which is  so this issue of the  legalistic aspects of the pre - sent you know pre - adapted Yeah , well , so what  is  the  the data that you take into SRI , first first question , you 're maintaining it in in a place that wouldn't be publicly readable that that kind of stuff , right ? phd a: U  phd c: From the outside world or postdoc g: By  people  who are not associated with this project . Although I sh  grad e: That that 's not the issue , phd c: de audio data itself ? grad e: it 's just the audio data itself , until people have a chance to edit it . phd c: Right now they 're not they 're in the speech group directories which so I will postdoc g: Great . professor b: Yeah so we just have to go through this process of having people approve the transcriptions , phd c: Yeah OK . postdoc g: Yeah , we had to get them to approve em and then i cuz cuz the other question I was gonna ask is if we 're having  you know it 's but this this meeting that you have , no problem cuz I I well  I I speak for myself grad e: It 's us . postdoc g: but but I think that we didn't do anything that but well anyway so  I wouldn't be too concerned about it with respect to that although we should clear it with Eric and Dan of course but these results are based on data which haven't had the  haven't had the chance to be reviewed by the subjects phd c: That 's true . postdoc g: and I don't know how that stands ,  if you if you get fantastic results and it 's involving data which which later end up being lessened by , you know certain elisions , then I don't know but I wanted to raise that issue , professor b: Well we , postdoc g: that 's all . professor b:  once we get all this streamlined it may be sh it hopefully it will be fairly quick but we get the transcriptions , people approve them and so on it 's just that we 're grad e: Alright we need to work at a system for doing that approval so that we can send people the transcripts postdoc g: Great . grad e: and get back any bleeps that they want phd c: Yeah actually the bleeps are also an issue I thought . professor b: It 's gonna be a rare thing that there 's a bleep for the most part . phd a: U  actually I had a question about the downsampling ,  I don't know who ,  how this was done but is is there are there any  issues with downsampling phd c: Don did this . phd a: because I know that the recognizer  that we use h can do it sort of on the fly  so we wouldn't have to have it eh you know do it  explicitly beforehand . And is there any  i are there other d sev  is there more than one way to do the downsampling where one might be better than another ? grad f: There are lots of w there are lots of ways to do the downsampling  different filters to put on , phd a: OK . phd a: So so the th grad e: I don't think we even know which one I assume you 're using syncat to do it ? grad f: No , I 'm using  SN SND  are resample . grad e: Or sound resample ? phd c: Re - re ref grad e: Resample . phd a: So so the other thing we should try is to just take the original wave forms , grad e: And a few of the consonants . phd c: Yeah we could we could try that and and compare phd a: And and feed them to feed them to the SRI recognizer and see if if the SRI front - end does something . I I only downsampled them first cuz I was phd a: Well grad f:  that 's just one line that 's one line of code to comment at phd c: yeah phd a: Right and and it doesn't is no more work for  you know for us . phd c: Well they 're just bigger to transfer , that 's why I s downsampled them before but phd a: Well but they 're only twice as big so phd c: Well  that was if it 's the same then we can downsample here phd a:  it 's it 's just a phd c: but if it 's grad f: Although those eighty meg files take a while to copy into my directories phd c: Yeah . grad f: so , but no ,  it 's not i it wouldn't be a problem if you 're interested in it phd c: We could try that . phd a: Yeah  it would be  you know it would probably take  about  you know grad f: it would phd a: minus the transfer time it would it would take  you know ten minutes to try and and and grad f: Yeah . grad e: It 's about a fifty minute drive , right ? phd a: And and if for some reason we see that it works better then we might investigate why phd c: Well it takes more disk space too so I was just phd a: and , you know , what Yeah . professor b: So you just train just different filters grad f: Yeah , I professor b: and so you 're just wondering whether the filter is grad f: Yeah , I can imagine it would be phd a: Right . phd c: So we could try that with this particular twenty minutes of speech and sort of see if there 's any differences . grad f:  I guess there 's some phd a: You know a at some point someone might have optimized whatever filtering is done for the actual recognition  performance . phd a: so grad e: It just seems to me that , you know small changes to the language model and the vocabulary will so swamp that that it may be premature to worry about that .  so one is a half a percent better than the other I don't think that gives you any information . phd c: Well it 's just as easy to to give you the sixteen K individual , grad e: Yep . phd c: it was just more disk space you know for storing them professor b: Are you are you using   mel cepstrum or PLP over there ? phd c: so phd a: Mel cepstrum . professor b: But but it wouldn't hurt to try , phd c: Could easily try phd a: That 's what I would assume but you never know , professor b: yeah . professor b: PLP uses  auto - regressive filtering and  modeling and so it can be sensitive to the kind of filtering that you 're doing phd a:   professor b: but   mel cepstrum  might not b you wouldn't expect to be so much but phd c: Well we can try it if you generate like the same set of files just up to that point where we stopped anyway and just sti stick them somewhere grad f: Yeah , it 's it 's really not a problem . Don't stop at that part because we 're actually using the entire conversation to estimate the speaker parameters , grad f: Keep going . phd a: so shouldn't use you should s you know , get grad f: Yeah ,  I 'll I have to do is eh e the reference file would stay the same , phd c: OK . grad f: it 's just the individual segments would be approximately twice as long phd a: Mmm . grad f: and I could just replace them with the bigger ones in the directory , phd a: Right . phd c:  I hand - edited the whole the whole meeting so that can be run it 's just Once we get the the bug out . postdoc g: One one question which is I I had the impression from this from this meeting that w that I transcribed that  that there was already automatic downsampling occurring , phd a: Yeah . postdoc g: so it was so it 's like there 's already down grad e: There 's one level that 's already happening right here . professor b: so phd c: And that 's actually said in your meeting , grad f:  . phd c: I I It 's like are we downsampling to sixteen ? professor b: It 's a digital audio orientation for the board phd c: Right . professor b: it 's in the monitor so it 's phd c: Thank God it 's not more than that . grad e: And I have no idea what filter it 's using , grad f: Is eight kilohertz is is eighty kilohertz generally accepted as like standard for voice ? grad e: so professor b: For telephone stuff . grad f: Yeah that 's what I was gonna say ,  like professor b: So it 's it 's it 's just that they were operating from Switchboard which was a completely telephone database grad f: so Oh , I see , so . professor b: Sixteen is more common for for  broadband stuff that isn't grad f: Right . professor b: that isn't music and isn't telephone , phd c: And I guess if you 're comparing like  if you wanna run recognition on the PZM stuff you would want you don't want to downsample the wh that professor b: yeah . phd c: right ? Well I don  if it 's any better professor b: No actually I would think that you would you would get better you 'd get better high frequencies in the local mike . professor b:  but who knows ?  we do we we we we we wanna find all this stuff out , phd c: Yeah well we could try it . grad e: We 're gonna have plenty of low frequency on the P Z Ms with the fans . phd c: Oh yeah there was just one more thing I wanted to say which is totally unrelated to the recognition except that  well well it 's sort of related but  good news also  I got well Chuck Fillmore agreed to record meetings but he had too many people in his meetings and that 's too bad cuz they 're very animated and but  Jerry also agreed so  we 're starting on on phd a: They 're less animated . phd c: Well but he has fewer he he won't have more than eight and it 's a meeting on even deeper understanding , EDU , so that sounds interesting . As a compliment to our front - end meeting grad e: Dot EDU ? phd c: and  so that 's gonna start Monday and one of the things that I was realizing is  it would be really great if anyone has any ideas on some kind of time synchronous way that people in the meeting can make a comment to the person whose gonna transcribe it or or put a push a button or something when they wanna make a note about " oh boy you should probably erase those last few " or  " wait I want this not to be recorded now " or  something like that s professor b: Weren't we gonna do something with a pad at one point ? postdoc g: The cross pads ? grad e: Yeah , we could do it with the cross pads . phd c: Cuz I was thinking you know if if the person who sets up the meeting isn't there and it 's a group that we don't know  and this came up talking to to Jerry also that you know is there any way for them to indicate to make sure that the qu request that they have that they make explicitly get addressed somehow professor b: Yeah . phd c: so I don't know if anyone has ideas or you could even write down " oh it 's about three twenty five and " professor b: Well what I was just suggesting is is we have these this cross pad just for this purpose grad e: Yeah , and use that . professor b: I don't know if you know this or if it 's a question for the mail to Dan but is this thing of two eight channel boards a maximum for this setup or could we go to a third board ? grad e: I don't know . I I think that it 's the maximum we can do without a lot of effort because it 's one board with two digital channels . And so w I think if we wanna do that more than that we 'd have to have two boards , and then you have the synchronization issue . professor b: But that 's a question because that would if it was possible cuz it is i you know already we have a a a group of people in this room that cannot all be miked grad e: Right . professor b: and it 's not just cuz we haven't been to the store , right it 's phd d: What is the limit on each of those f fiber channels , is it the grad e: Eight . phd d: It just it 's eight channels come in , does it have do with the sampling rate ? grad e: It 's eight . But each each fiber channel has eight eight channels and there are two ch two fibers that go in to the card . professor b: It might be a hard limitation , grad e: So professor b:  one thing is it the whole thing as I said is is all structured in terms of forty - eight kilohertz sampling so that pushes requirements up a bit grad e: Yeah . grad e:  then we 'd also have to get another ADD and another mixer and all that sort of stuff . grad e: OK on the  are we done with that ? So the oth topic is  getting more mikes and different mikes , so I got a quote  We can fit we have room for one more wireless and the wireless , this unit here is three fifty three hundred fifty dollars , it I didn't realize but we also have to get a tuner the receiver the other end , that 's  four thirty  and then also phd c: For for each ? phd d: Wow . grad e: And we just need one more so so professor b: Yeah at least w we got the good ones . grad e:  and then also  It turns out that the connector that this thing uses is proprietary of Sony phd d: Oh . grad e: So if we wanna use a different set headset the solution that the guy suggested and they apparently lots of people have done is Sony will sell you the jack with just wires coming out the end and then you can buy a headset that has pigtail and solder it yourself . And that 's the other solution and so the jacks are forty bucks apiece and the he recommended  a crown CM three eleven AE headset for two hundred bucks apiece . professor b: There isn't this some sort of thing that plugs in , you actually have to go and do the soldering yourself ? grad e: Becau - the reason is the only only thing you can get that will plug into this is this mike or just the connector . The reason I ask is these sort of handmade  wiring jobs fall apart in use so the other thing is to see if we can  get them to do a custom job and put it together for this . grad e: Oh I 'm sure they would , they would just charge us , phd d: Well , and they 'd probably want quantity too , grad e: so . professor b: Well phd d: they 'd professor b: no they 'll just charge us more , so it 's this phd d: Mmm . grad e: So so my question is should we go ahead and get na nine identical head - mounted crown mikes ? professor b: Not before having one come here and have some people try it out . professor b: Because there 's no point in doing that if it 's not gonna be any better . grad e: So why don't we get one of these with the crown with a different headset ? professor b: Yeah . professor b: And see if it 's preferable and if it is then we 'll get more . phd c: Cuz  I think the microphones are OK it 's just the the grad e: Right , it 's just they 're not comfortable to wear . phd c: Could make our own handbands and grad e: and he said they don't have any of these in stock but they have them in LA and so it will take about a week to get here . professor b: Yeah well it 's grad e:  so OK to just go order ? professor b: We 're in this for the long term , yeah . grad e: and who is the contact if I wanna do an invoice grad f: Yeah . professor b:  y right cuz so one is for the daisy chain so that 's fifteen instead of sixteen grad e: Without getting more stuff . phd c: Is is there any way we can have you know like a a wireless microphone that you pass around to the people who you know the extra people for the times they wanna talk that grad e: Probably . phd c:  professor b: That 's not a dumb question , it 's a good idea , phd c: Well  phd a: Like  like you know Jerry Springer thing , professor b: yeah . grad e: I 'm just not sure how we would handle that in the grad f: That 's like the Conch . phd c: Well but phd d: Like at conferences phd a: you know r phd c: well but there might be a way to say that there are gonna be these different people grad f: See , look . phd c:  and I don't know identifying somehow ? phd d: so nail the chairs down . professor b: No that no no phd a:  professor b: that 's a very if we can't get another board and even if we can I have a feeling they 'll be some work . professor b: Let 's figure that we have eight which are set up and then there 's a ninth which is passed around to grad e: A hand - held , yeah . Kind of rules out overlap but but  phd c: Well or also for you know if people are not professor b: Yeah . professor b:  no no that 's grad e: Rather than get a phd c: No not the lapel . grad e: Well  is the is the hand - held really any better ? phd d: Liz hates the lapel . professor b: No it no it depends on the hand - held grad e: Is awful ? professor b: but hand many hand - helds are built wi with sort of  anti - shock sort of things so that it it is less  susceptible to hand noises . They have What ? grad e: I wonder if they have one that will hook up to this or whether again we 'll have to wire it ourselves . phd d: Well , you wouldn't want it to hook there you 'd just want it to hook into the receiver in the other room , right ? professor b: No that 's  you need a transmitter . grad e: What ? phd d: Is th isn't that built into the mike ? professor b: Oh I see . phd c: Yeah just these ones that they pass around with no you know wireless professor b: Yeah . grad e: It 's gonna be much easier to get one of these and just plug in a mike , isn't it ? phd d: But then the mike has to h phd a: Do you have to hand it around and if you have two pieces of professor b: No no phd c: Right . phd a:  professor b: so right , so this is a good point , so yeah you have these these mikes with a little antenna on the end right ? grad e: OK . And do you think you would be able to use the same receiver ? professor b: I don't know . You 'll have to check with them , grad e: OK I 'll I 'll ask . professor b: and then just sort of have that as the and then you can have groups of twenty people or whatever and and  phd c: Yeah because there 's only  as Andreas pointed out actually I think in the large the larger the group the less interaction the less people are talking  over each other phd a: Pretty soon . phd c: it just there might be a lot of people that speak once or twice and professor b: Right . grad e: OK so I guess people who have to leave can leave and do we have anything else to discuss or should we just do digits ? postdoc g: I I thought of some extra a couple of extra things I 'd like to mention . postdoc g: One of them is to give you a status in terms of the transcriptions so far . So  as of last night  I 'd assigned twelve hours and they 'd finished nine grad e:  Yep , postdoc g: and my goal was to have eleven done by the end of the month , I think that by tomorrow we 'll have ten . phd c:  it 's great professor b: Pretty close , postdoc g: So they 're still working . phd c: I j and this I got this email from Jane at like two in the morning or something phd d: Wow . phd c: so it 's really great postdoc g: It 's working out , thanks . And then  also an idea for another meeting , which would be to have the transcribers talk about the data It 's sort of a a little bit a little bit phd c: That 's a great idea . phd c: That 's a great idea cuz I 'd like to g have it recorded so that we can remember all the little things , grad f: Yeah . phd d: So if we got them to talk about this meeting , it would be a meta meta meeting . Yeah , exa exactly I guess nested several layers , professor b: Now you have eight transcribers and there 's ten of us postdoc g: but professor b: so how do we do this , is the only thing . phd c: And have postdoc g: Well that 's what I 'm thinking , professor b: Oh . Have them talk about the data and they and they 've made observations to me phd c: that would be great . postdoc g: like they say  you know this meeting that we think has so much overlap , in fact it does but there are other groups of similar size that have very little , you know it 's part of it 's it 's the norm of the group and all that and they have various observations that would be fun , I think . professor b: So maybe we could they could have a meeting more or less without us that to do this and we should record it postdoc g: OK . professor b: and then maybe one or two of them could come to one of these meetings and and could you know could tell us about it . phd c: It 's they will get to transcribe their own meeting but they also get paid for having a break grad e: That would be weird . phd c: and I think that 's a good idea , postdoc g: Yeah exactly , yeah . postdoc g: And then I wanted to also  say something about the Fiscus   John John Fiscus visit tomorrow . And Which is to say that w it 'll be from nine to one that I 'm going to   offer the organization allow him to  adjust it if he wishes but to be basically in three parts , the acoustic part coming first which would be basically the room engineering aspects  other things and he 'll be also presenting what NIST is doing and and  then  number two would be sort of a the the transcription process so this would be a focus on like presegmentation and the modifications to the the multitrans interface which allows more refined encoding of the beginnings and ends of the overlapping segments which  Dave Gelbart 's been doing and then   and of course the presegmentation Thilo 's been doing and then  the third part would and again he has some stuff that 's i relevant with respect to NIST and then the third one would be focus on transcription standards so at NIST he 's interested in this establishment of a global encoding standard I guess I would say and I want it , you know k yeah see what they 're doing and also present what what we 've chosen as ours and and discuss that kind of thing . And so but he 's only here until until one and actually we 're thinking of noon being  lunch time so basically hoping that we can get as much of this done as possible before noon . So grad e: Oh , where you 're gonna meet ? postdoc g: yeah . Here mostly but I 've also reserved the BARCO room  eh to figure out how that works in terms of like maybe having a live demonstration . professor b: OK but the nine o ' cl nine o ' clock will be i be in here . grad e: I assume we 're not gonna try to record it ? postdoc g: Oh I think that would be hard , yeah . professor b: So maybe do digits and recess ? grad e: Unless there 's anything else ? postdoc g: Yeah . grad e:  should y we make him wear Andreas ' mike or would that just be too confusing ? professor b: Yeah . postdoc g: When we do this in the key in the key in the key it has to indicate that channel change , phd d: Does it mess up the forms ? postdoc g: right ? grad e:  yeah I just don't know how we would do that , so . Currently I 'm taking  my first graduate level courses in DSP and  when I come back to Norway I 'm gonna continue with the more of a research project work kind of work . So this semester I 'm starting up with a with a small project through  Dave Gelbart which I 'm taking a course with I got in touch with him and he told me about this project . What I 'm gonna try to do is  use use ech echo cancellation to  to handle the periods where you have overlapping talk . So currently I 'm  I 'm just reading up on echo cancellation , s looking into the theory behind that and then  hopefully I get some results . grad e: And are you staying at Berkeley or is are you just here a semester ? grad j: This is my second semester and last . grad j: So I leave professor b: He 's in the he 's in the cour two two five D course . grad j: Yeah , I 'm in Morgan 's course , professor b: So , yeah . About doing some of the echo cancellation stuff or possibly the spectroanalysis over the overlaps , so


professor c: So ,  grad f: so I wanted to discuss digits briefly , but that won't take too long . OK , agenda items , we have digits , What else we got ? phd a: New version of the presegmentation . postdoc b: do we wanna say something about the , an update of the , transcript ? phd g: Yeah , why don't you summarize the professor c: Update on transcripts . phd g: And I guess that includes some the filtering for the , the ASI refs , too . professor c: Filtering for what ? phd g: For the references that we need to go from the the fancy transcripts to the sort of brain - dead . postdoc b: It 'll it 'll be basically it 'll be a re - cap of a meeting that we had jointly this morning . OK well , the , w  as you can see from the numbers on the digits we 're almost done . You know , someone fills out the form and then they 're not at the meeting and so it 's blank . And so , once we 're it 's done it would be very nice to train up a recognizer and actually start working with this data . phd d: So we 'll have a corpus that 's the size of TI - digits ? grad f: And so One particular test set of TI - digits . grad f: So , I I extracted , Ther - there was a file sitting around which people have used here as a test set . It had been randomized and so on phd d:  grad f: and that 's just what I used to generate the order . professor c: So , I 'm impressed by what we could do , Is take the standard training set for TI - digits , train up with whatever , you know , great features we think we have ,  for instance , and then test on  this test set . grad f:  professor c: And presumably  it should do reasonably well on that , and then , presumably , we should go to the distant mike , and it should do poorly . professor c: And then we should get really smart over the next year or two , and it that should get better . grad f: so that the reason it 's not just a transcript is that there 're false starts , and misreads , and miscues and things like that . And so I have a set of scripts and X Waves where you just select the portion , hit R , it tells you what the next one should be , and you just look for that . You know , so it it 'll put on the screen , " The next set is six nine , nine two two " . And you find that , and , hit the key and it records it in a file in a particular format . professor c: So is this grad f: And so the the question is , should we have the transcribers do that or should we just do it ? Well , some of us . I 've been do I 've done , eight meetings , something like that , just by hand .  professor c: what what do you think ? postdoc b: My feeling is that we discussed this right before coffee and I think it 's a it 's a fine idea partly because , it 's not un unrelated to their present skill set , but it will add , for them , an extra dimension , it might be an interesting break for them . And also it is contributing to the , c composition of the transcript cuz we can incorporate those numbers directly and it 'll be a more complete transcript . grad f: There is there is professor c: So you think it 's fine to have the transcribers do it ? postdoc b:   grad f: There 's one other small bit , which is just entering the information which at s which is at the top of this form , onto the computer , to go along with the where the digits are recorded automatically . grad f: And so it 's just , you know , typing in name , times time , date , and so on .  , which again either they can do , but it is , you know , firing up an editor , or , again , I can do . postdoc b: And , that , you know , I 'm not , that that one I 'm not so sure if it 's into the the , things that , I , wanted to use the hours for , because the , the time that they 'd be spending doing that they wouldn't be able to be putting more words on . postdoc b: But that 's really your choice , it 's your phd d: So are these two separate tasks that can happen ? Or do they have to happen at the same time before grad f: No they don't have this you have to enter the data before , you do the second task , but they don't have to happen at the same time . grad f: So it 's it 's just I have a file whi which has this information on it , and then when you start using my scripts , for extracting the times , it adds the times at the bottom of the file . And so , it 's easy to create the files and leave them blank , and so actually we could do it in either order . grad f: it 's it 's sort of nice to have the same person do it just as a double - check , to make sure you 're entering for the right person . Yeah just by way of  , a  , order of magnitude , we 've been working with this Aurora ,  data set . And , the best score , on the , nicest part of the data , that is , where you 've got training and test set that are basically the same kinds of noise and so forth , is about ,  I think the best score was something like five percent , error , per digit . So So the The point there , and this is  car noise  ,  things , but but real real situation , phd d:   professor c: well , " real " , the  there 's one microphone that 's close , that they have as as this sort of thing , close versus distant .  but in a car , instead of instead of having a projector noise it 's it 's car noise . So , that 's that 's an indication ,  that was with , many sites competing , and this was the very best score and so forth , so . More typical numbers like phd d: Although the models weren't , that good , right ?  , the models are pretty crappy ? professor c: You 're right . I think that we could have done better on the models , but the thing is that we got this this is the kind of typical number , for all of the , things in this task , all of the , languages . Anyway , just an indication once you get into this kind of realm even if you 're looking at connected digits it can be pretty hard . phd d: How did we do on the TI - digits ? grad f: Well the prosodics are so much different s it 's gonna be , strange . grad f: So I 'm I 'm not sure how much of effect that will have . phd d: H how do phd g: What do you mean , the prosodics ? grad f: just what we were talking about with grouping . That with these , the grouping , there 's no grouping at all , and so it 's just the only sort of discontinuity you have is at the beginning and the end . phd g: So what are they doing in Aurora , are they reading actual phone numbers , grad f: Aurora I don't know . phd g: or , a a digit at a time , or ? professor c: I 'm not sure how phd g: Cuz it 's professor c: no , no  it 's connected it 's connected , digits , phd g: Connected . phd g: So there 's also the not just the prosody but the cross the cross - word modeling is probably quite different . phd d: H How grad f: But in TI - digits , they 're reading things like zip codes and phone numbers and things like that , phd g: Right . phd d: do we do on TI - digits ? grad f: so it 's gonna be different . grad f: One and a half percent , two percent , something like that ? professor c: I th no I think we got under a percent , but it was but it 's but  . The very best system that I saw in the literature was a point two five percent or something that somebody had at at Bell Labs , or . It s strikes me that there are more each of them is more informative because it 's so , random , grad f: OK . professor c: But I think a lot of systems sort of get half a percent , or three - quarters a percent , grad f: Right . grad f: But that  it 's really it 's it 's close - talking mikes , no noise , clean signal , just digits , every everything is good . phd g: It 's like the , single cell , you know , it 's the beginning of life , phd d: Pre - prehistory . grad f: OK , so , what I 'll do then is I 'll go ahead and enter , this data . And then , hand off to Jane , and the transcribers to do the actual extraction of the digits . One question I have that that  , we wouldn't know the answer to now but might , do some guessing , but I was talking before about doing some model modeling of arti  , marking of articulatory , features , with overlap and so on . The only thing is I 'm a little concerned that maybe the kind of phenomena , in w i i The reason for doing it is because the the argument is that certainly with conversational speech , the stuff that we 've looked at here before , just doing the simple mapping , from , the phone , to the corresponding features that you could look up in a book , isn't right . In fact there 's these overlapping processes where some voicing some up and then some , you know , some nasality is comes in here , and so forth . But , It could be that when we 're reading digits , because it 's it 's for such a limited set , that maybe maybe that phenomenon doesn't occur as much . Di - an anybody ? Do you have any ? Anybody have any opinion about that , postdoc b: and that people might articulate more , and you that might end up with more a closer correspondence . phd d: Sort of less predictability , grad f: That it 's just postdoc b:   phd d: and You hafta grad f: It 's a Well Would , this corpus really be the right one to even try that on ? phd g: Well it 's definitely true that , when people are , reading , even if they 're re - reading what , they had said spontaneously , that they have very different patterns . phd g: So the fact that they 're reading , first of all , whether they 're reading in a room of , people , or rea you know , just the fact that they 're reading will make a difference . So , may maybe the thing will be do to take some very small subset ,  not have a big , program , but take a small set , subset of the conversational speech and a small subset of the digits , and look and and just get a feeling for it . postdoc b: H That could could be an interesting design , too , cuz then you 'd have the com the comparison of the , predictable speech versus the less predictable speech professor c: Cuz I don't think anybody is , I at least , I don't know , of anybody , well , I don't know , the answers . postdoc b: and maybe you 'd find that it worked in , in the , case of the pr of the , non - predictable . phd d: Hafta think about , the particular acoustic features to mark , too , because , some things , they wouldn't be able to mark , like , you know , tense lax . grad f: M I think we can get Ohala in to , give us some advice on that . postdoc b: Also I thought you were thinking of a much more restricted set of features , that professor c: Yeah , but I I I I was , like he said , I was gonna bring John in and ask John what he thought . It should be such that if you , if you , if you had o  , all of the features , determined that you that you were  ch have chosen , that that would tell you , in the steady - state case , the phone . grad f: Even , I guess with vowels that would be pretty hard , wouldn't it ? To identify actually , you know , which one it is ? postdoc b: It would seem to me that the points of articulation would be m more , g  ,  that 's I think about articulatory features , I think about , points of articulation , which means , rather than vowels . phd d: Points of articulation ? What do you mean ? postdoc b: So , is it , bilabial or dental or is it , you know , palatal . phd g: Well it 's also , there 's , really a difference between , the pronunciation models in the dictionary , and , the pronunciations that people produce . And , so , You get , some of that information from Steve 's work on the on the labeling professor c: Right . That maybe , although I think the meeting context is great , that he has transcriptions that give you the actual phone sequence . And you can go from not from that to the articulatory features , but that would be a better starting point for marking , the gestural features , then , data where you don't have that , because , we you wanna know , both about the way that they 're producing a certain sound , and what kinds of , you know what kinds of , phonemic , differences you get between these , transcribed , sequences and the dictionary ones . professor c: Well you might be right that mi might be the way at getting at , what I was talking about , but the particular reason why I was interested in doing that was because I remember , when that happened , and , John Ohala was over here and he was looking at the spectrograms of the more difficult ones . One you know , one is going from a dictionary pronunciation of something , like , " gonna see you tomorrow " , grad f: And Or " gonta " . phd g: it could be " going to " or " gonna " or " gonta s " you know . And , that it would be nice to have these , intermediate , or these some these reduced pronunciations that those transcribers had marked or to have people mark those as well . phd g: Because , it 's not , that easy to go from the , dictionary , word pronuncia the dictionary phone pronunciation , to the gestural one without this intermediate or a syllable level kind of , representation . grad f: Well I don't think Morgan 's suggesting that we do that , though . professor c: Yeah , I I I 'm jus at the moment of course we 're just talking about what , to provide as a tool for people to do research who have different ideas about how to do it . So for instance , you might have someone who just has a wor has words with states , and has   , comes from articulatory gestures to that . But  , grad f: But What I 'm imagining is a score - like notation , where each line is a particular feature . grad f: so you would say , you know , it 's voiced through here , and so you have label here , and you have nas nasal here , and , they they could be overlapping in all sorts of bizarre ways that don't correspond to the timing on phones . professor c:  this is the kind of reason why I remember when at one of the Switchboard , workshops , that  when we talked about doing the transcription project , Dave Talkin said , " can't be done " . professor c: He was he was , what what he meant was that this isn't , you know , a sequence of phones , and when you actually look at Switchboard that 's , not what you see , and , you know . It , grad f: And in in fact the inter - annotator agreement was not that good , right ? On the harder ones ? professor c: yeah  it was phd g: It depends how you look at it , and I I understand what you 're saying about this , kind of transcription exactly , professor c: Yeah . phd g: because I 've seen you know , where does the voicing bar start and so forth . phd g: All I 'm saying is that , it is useful to have that the transcription of what was really said , and which syllables were reduced .  , if you 're gonna add the features it 's also useful to have some level of representation which is , is a reduced it 's a pronunciation variant , that currently the dictionaries don't give you professor c:   phd g: because if you add them to the dictionary and you run recognition , you , you add confusion . phd d: So it would be it would be great if we had , either these kind of , labelings on , the same portion of Switchboard that Steve marked , or , Steve 's type markings on this data , with these . phd g: And Steve 's type is fairly it 's not that slow , I dunno exactly what the , timing was , but . professor c: Yeah u I don't disagree with it the on the only thing is that , What you actually will end en end up with is something , i it 's all compromised , right , so , the string that you end up with isn't , actually , what happened . But it 's it 's the best compromise that a group of people scratching their heads could come up with to describe what happened . And it 's more accurate than the than the dictionary or , if you 've got a pronunciation  lexicon that has three or four , grad f: The word . professor c: this might be have been the fifth one that you tr that you pruned or whatever , phd d: So it 's like a continuum . phd g: an and in some places it would fill in , So the kinds of gestural features are not everywhere . phd g: So there are some things that you don't have access to either from your ear or the spectrogram , phd d:   phd g: but you know what phone it was and that 's about all you can all you can say . phd g: And then there are other cases where , nasality , voicing phd d: It 's basically just having , multiple levels of of , information and marking , on the signal . grad f: Well the other difference is that the the features , are not synchronous , phd g: Right . phd g: Th - there 'll be no way for you to actually mark what was said completely by features . grad f: Well not with our current system but you could imagine designing a system , that the states were features , rather than phones . phd g: And i if you 're Well , we we 've probably have a separate , discussion of ,  of whether you can do that . postdoc b: That 's Well , isn't that I thought that was , well but that wasn't that kinda the direction ? grad f: Yeah . postdoc b: I thought professor c: Yeah , so  , what , what where this is , I I want would like to have something that 's useful to people other than those who are doing the specific kind of research I have in mind , so it should be something broader . But , The but  where I 'm coming from is , we 're coming off of stuff that Larry Saul did with with , John Dalan and Muzim Rahim in which , they , have , a m a multi - band system that is , trained through a combination of gradient learning an and EM , to  , estimate , the , value for m for for a particular feature . And this is part of a larger , image that John Dalan has about how the human brain does it in which he 's sort of imagining that , individual frequency channels are coming up with their own estimate , of of these , these kinds of something like this . And the the th this particular image , of how thi how it 's done , is that , then given all of these estimates at that level , there 's a level above it , then which is is making , some kind of sound unit classification such as , you know , phone and and , you know . But that that 's sort of what I was imagining doing , and but it 's still open within that whether you would have an intermediate level in which it was actually phones , or not .  , but , Again , I wouldn't wanna , wouldn't want what we we produced to be so , know , local in perspective that it it was matched , what we were thinking of doing one week , And and , and , you know , what you 're saying is absolutely right . That , that if we , can we should put in , another level of , of description there if we 're gonna get into some of this low - level stuff . phd d: Well , you know ,   if we 're talking about , having the , annotators annotate these kinds of features , it seems like , You know , you The the question is , do they do that on , meeting data ? Or do they do that on , Switchboard ? grad f: That 's what I was saying , postdoc b: W Well it seems like you could do both . postdoc b: I was thinking that it would be interesting , to do it with respect to , parts of Switchboard anyway , in terms of , professor c:   postdoc b:  partly to see , if you could , generate first guesses at what the articulatory feature would be , based on the phone representation at that lower level . phd d: Well cuz the yeah , and then also , if you did it on Switchboard , you would have , the full continuum of transcriptions . phd d: You 'd have it , from the lowest level , the ac acoustic features , then you 'd have the , you know , the phonetic level that Steve did , postdoc b:   phd g: it is telephone band , so , the bandwidth might be phd d: It 'd be a complete , set then . So  i we 'll see wha how much we can , get the people to do , and how much money we 'll have and all this sort of thing , phd g: Yeah . phd d: But it it might be good to do what Jane was saying  , you know , seed it , with , guesses about what we think the features are , based on , you know , the phone or Steve 's transcriptions or something . grad f: Alright , so based on the phone transcripts they would all be synchronous , but then you could imagine , nudging them here and there . professor c: Well I think what  I 'm I 'm a l little behind in what they 're doing , now , and , the stuff they 're doing on Switchboard now . But I think that , Steve and the gang are doing , something with an automatic system first and then doing some adjustment . So  that 's probably the right way to go anyway , is to is to start off with an automatic system with a pretty rich pronunciation dictionary that , that , you know , tries , to label it all . postdoc b: So in in our case you 'd think about us s starting with maybe the regular dictionary entry , and then ? Or would we professor c: Well , regular dictionary , this is a pretty rich dictionary . It 's got , got a fair number of pronunciations in it postdoc b: But phd d: Or you could start from the if we were gonna , do the same set , of sentences that Steve had , done , we could start with those transcriptions . phd g: the problem is when you run , if you run a regular dictionary , even if you have variants , in there , which most people don't , you don't always get , out , the actual pronunciations , phd d: Yeah . phd g: so that 's why the human transcriber 's giving you the that pronunciation , postdoc b: Yeah . phd g: and so y they they I thought that they were professor c: Is that what they 're doing ? grad f: They are . professor c: Yeah , so I think that i i we also don't have , we 've got a good start on it , but we don't have a really good , meeting , recorder or recognizer or transcriber or anything yet , so . So ,  another way to look at this is to , is to , do some stuff on Switchboard which has all this other , stuff to it . professor c: And then , As we get , further down the road and we can do more things ahead of time , we can , do some of the same things to the meeting data . postdoc b: And I 'm and these people might they they are , s most of them are trained with IPA . professor c: Yeah postdoc b: They 'd be able to do phonetic - level coding , or articulatory . phd d: Are they busy for the next couple years , or ? postdoc b: Well , you know ,  they , they they 're interested in continuing working with us , so  I , and this would be up their alley , so , we could when the when you d meet with , with John Ohala and find , you know what taxonomy you want to apply , then , they 'd be , good to train onto it . phd g: It might be grad f:  you 'd you 'd want models for spreading . phd g: I was thinking it might be n phd d: Of the f acoustic features ? grad f: Yeah . phd g: Well it might be neat to do some , phonetic , features on these , nonword words . Are are these kinds of words that people never the " huh "s and the "  "s and the " huh " and the  These k No , I 'm serious . phd g: And some of them are , yeah , "  - huh "s , and "  "s , and , "  ! " "  " " OK " , "  " Grunts , that might be interesting . professor c: new version of , presegmentation ? phd a: oh yeah , I worked a little bit on the on the presegmentation to to get another version which does channel - specific , speech - nonspeech detection . And , what I did is I used some normalized features which , look in into the which is normalized energy , energy normalized by the mean over the channels and by the , minimum over the , other . And to to , to , yeah , to normalize also loudness and and modified loudness and things and that those special features actually are in my feature vector . phd a: And , and , therefore to be able to , somewhat distinguish between foreground and background speech in in the different in each channel . And , eh , I tested it on on three or four meetings and it seems to work , well yeah , fairly well , I I would say . grad f: So I I understand that 's what you were saying about your problem with , minimum . phd a: Yeah yeah , then I I did some some some things like that , postdoc b: Interesting . phd a: as there there are some some problems in , when , in the channel , there they the the speaker doesn't doesn't talk much or doesn't talk at all . Then , the , yeah , there are there are some problems with with with n with normalization , and , then , there the system doesn't work at all . So , I 'm I 'm glad that there is the the digit part , where everybody is forced to say something , professor c: Right . And , the thing is I I , then the evaluation of of the system is a little bit hard , as I don't have any references . phd a: Yeah , that 's the one one wh where I do the training on so I can't do the evaluation on So the thing is , can the transcribers perhaps do some , some some meetings in in terms of speech - nonspeech in in the specific channels ? grad f:  . postdoc b: Well , I have phd d: Well won't you have that from their transcriptions ? postdoc b: Well , OK , so , now we need grad f: No , cuz we need is really tight . postdoc b: so , I think I might have done what you 're requesting , though I did it in the service of a different thing . postdoc b: I have thirty minutes that I 've more tightly transcribed with reference to individual channels . postdoc b: And I could And And grad f: Hopefully that 's not the same meeting that we did . postdoc b: So , e so the , you know , we have the , th they transcribe as if it 's one channel with these with the slashes to separate the overlapping parts . postdoc b: And then we run it through then it then I 'm gonna edit it and I 'm gonna run it through channelize which takes it into Dave Gelbart 's form format . postdoc b: And then you have , all these things split across according to channel , and then that means that , if a person contributed more than once in a given , overlap during that time bend that that two parts of the utterance end up together , it 's the same channel , phd a: OK . postdoc b: and then I took his tool , and last night for the first thirty minutes of one of these transcripts , I , tightened up the , boundaries on individual speakers ' channels , phd a: OK . postdoc b: cuz his his interface allows me to have total flexibility in the time tags across the channels . phd a: so , yeah yeah , that that that 's great , but what would be nice to have some more meetings , not just one meeting to to be sure that that , there is a system , phd d: So , current This week . grad f: Yeah , so if we could get a couple meetings done with that level of precision I think that would be a good idea .  , how how m much time so the meetings vary in length , what are we talking about in terms of the number of minutes you 'd like to have as your as your training set ? phd a: It seems to me that it would be good to have , a few minutes from from different meetings , so . postdoc b: OK , now you 're saying different meetings because of different speakers or because of different audio quality or both or ? phd a: Both both . professor c: Yeah , we don't have that much variety in meetings yet ,  we have this meeting and the feature meeting and we have a couple others that we have  , couple examples of . grad e: Even probably with the gains differently will affect it , you mean phd a: not really as professor c: Poten - potentially . phd g: We can try running we haven't done this yet because , Andreas an is is gonna move over the SRI recognizer . phd g: cuz we 're running the evals and I just don't have machine time there . But , once that 's moved over , hopefully in a a couple days , then , we can take , what Jane just told us about as , the presegmented , the the segmentations that you did , at level eight or som at some , threshold that Jane , tha right , and try doing , forced alignment . phd g: And if it 's good , then that will that may give you a good boundary . Of course if it 's good , we don't then we 're we 're fine , phd a: Yeah . M phd g: but , I don't know yet whether these , segments that contain a lot of pauses around the words , will work or not . phd a: I I would quite like to have some manually transcribed references for for the system , as I 'm not sure if if it 's really good to compare with with some other automatic , found boundaries . postdoc b: Well , no , if we were to start with this and then tweak it h manually , would that that would be OK ? phd a: Yeah . phd g: but , I would have maybe a transciber , look at the result of a forced alignment and then adjust those . phd g: If they 're horrible it won't help at all , but they might not be horrible . postdoc b: How many minutes would you want from  , we could easily , get a section , you know , like say a minute or so , from every meeting that we have so f from the newer ones that we 're working on , everyone that we have . phd a: If it 's not the first minute of of the meeting , that that 's OK with me , but , in in the first minute , Often there are some some strange things going on which which aren't really , well , for , which which aren't re re really good . What what I 'd quite like , perhaps , is , to have , some five minutes of of of different meetings , so . postdoc b: And , then I wanted to ask you just for my inter information , then , would you , be trai cuz I don't quite unders so , would you be training then , the segmenter so that , it could , on the basis of that , segment the rest of the meeting ? So , if I give you like five minutes is the idea that this would then be applied to , to , providing tighter time bands ? phd a: I I could do a a retraining with that , yeah . phd a: That 's but but I hope that I I don't need to do it . phd a: I 'm I 'm not sure , but , for for for those three meetings whi which I which I did , it seems to be , quite well , but , there are some some as I said some problems with the lapel mike , but , perhaps we can do something with with cross - correlations to , to get rid of the of those . Well well what I want to do is to to look into cross - correlations for for removing those , false overlaps . phd g: Are the , wireless , different than the wired , mikes , at all ?  , have you noticed any difference ? phd a: I 'm I 'm not sure , if if there are any wired mikes in those meetings , or , I have have to loo have a look at them but , I 'm I 'm I think there 's no difference between , phd g: So it 's just the lapel versus everything else ? phd a: Yeah . postdoc b: OK , so then , if that 's five minutes per meeting we 've got like twelve minutes , twelve meetings , roughly , that I 'm that I 've been working with , then professor c: Of of of the meetings that you 're working with , how many of them are different , tha phd a: No . professor c: are there any of them that are different than , these two meetings ? postdoc b: Well oh wa in terms of the speakers or the conditions or the ? professor c: Yeah , speakers . postdoc b: just from what I 've seen , there are some where , you 're present or not present , and , then then you have the difference between the networks group and this group phd a: Yeah , I know , some of the NSA meetings , yeah . professor c: Do you have any of Jerry 's meetings in your , pack , er , postdoc b: no . professor c: No ? postdoc b: We could ,  you you recorded one last week or so . professor c: and and having as much variety for speaker certainly would be a big part of that I think . postdoc b: OK , so if I , OK , included include , OK , then , if I were to include all together samples from twelve meetings that would only take an hour and I could get the transcribers to do that right  , what  is , that would be an hour sampled , and then they 'd transcribe those that hour , right ? That 's what I should do ? professor c: Yeah . So they get it into the multi - channel format and then adjust the timebands so it 's precise . professor c: So that should be faster than the ten times kind of thing , postdoc b: Absolutely . postdoc b: Oh gosh , well , last night , I did about half an hour in , three hours , which is not , terrific , professor c: Yeah . postdoc b: but , anyway , it 's an hour and a half per professor c: Yeah . phd a: Do the transcribers actually start wi with , transcribing new meetings , or are they ? postdoc b: Well ,  they 're still working they still have enough to finish that I haven't assigned a new meeting , phd a: OK . postdoc b: but the next , m m I was about to need to assign a new meeting and I was going to take it from one of the new ones , phd a: OK . phd g: So they 're really running out of , data , prett  that 's good . professor c: They 're running out of data unless we s make the decision that we should go over and start , transcribing the other set . postdoc b: And so I was in the process of like editing them but this is wonderful news . postdoc b: We funded the experiment with ,  also we were thinking maybe applying that that to getting the , Yeah , that 'll be , very useful to getting the overlaps to be more precise all the way through . So , Liz , and and Don , and I met this morning , in the BARCO room , with the lecture hall , professor c: OK . postdoc b: and this afternoon , it drifted into the afternoon , concerning this issue of , the , well there 's basically the issue of the interplay between the transcript format and the processing that , they need to do for , the SRI recognizer . And , well , so , I mentioned the process that I 'm going through with the data , so , you know , I get the data back from the transcri Well , s  , metaphorically , get the data back from the transcriber , and then I , check for simple things like spelling errors and things like that . And , I 'm going to be doing a more thorough editing , with respect to consistency of the conventions . And , then , I run it through , the channelize program to get it into the multi - channel format , OK . And the , what we discussed this morning , I would summarize as saying that , these units that result , in a a particular channel and a particular timeband , at at that level , vary in length . But it 's really an empirical question , whether the units we get at this point through , just that process I described might be sufficient for them . So , as a first pass through , a first chance without having to do a lot of hand - editing , what we 're gonna do , is , I 'll run it through channelize , give them those data after I 've done the editing process and be sure it 's clean . And I can do that , pretty quickly , with just , that minimal editing , without having to hand - break things . postdoc b: And then we 'll see if the units that we 're getting , with the at that level , are sufficient . And if they do need to be further broken down then maybe it just be piece - wise , maybe it won't be the whole thing . So , that 's that 's what we were discussing , this morning as far as I Among phd g: Right . postdoc b: also we discussed some adaptational things , phd g: Then lots of postdoc b: so it 's like , phd g: Right . postdoc b:  You know I hadn't , incorporated , a convention explicitly to handle acronyms , for example , but if someone says , PZM it would be nice to have that be directly interpretable from , the transcript what they said , professor c:   It 's like y it 's and so , I 've I 've incorporated also convention , with that but that 's easy to handle at the post editing phase , and I 'll mention it to , transcribers for the next phase but that 's OK .  , and also I 'll be , encoding , as I do my post - editing , the , things that are in curly brackets , which are clarificational material . So , it 's gonna be either a gloss or it 's gonna be a vocal sound like a , laugh or a cough , or , so forth . Or a non - vocal sound like a doors door - slam , and that can be easily done with a , you know , just a one little additional thing in the , in the general format . phd g: Yeah we j we just needed a way to , strip , you know , all the comments , all the things th the that linguist wants but the recognizer can't do anything with .  , but to keep things that we mapped to like reject models , or , you know , mouth noise , or , cough . And then there 's this interesting issue Jane brought up which I hadn't thought about before but I was , realizing as I went through the transcripts , that there are some noises like , well the good example was an inbreath , where a transcriber working from , the mixed , signal , doesn't know whose breath it is , grad f: Right . phd g: and they 've been assigning it to someone that may or may not be correct . And what we do is , if it 's a breath sound , you know , a sound from the speaker , we map it , to , a noise model , like a mouth - noise model in the recognizer , and , yeah , it probably doesn't hurt that much once in a while to have these , but , if they 're in the wrong channel , that 's , not a good idea . And then there 's also , things like door - slams that 's really in no one 's channel , they 're like it 's in the room . phd g: And  , Jane had this nice , idea of having , like an extra ,  couple tiers , grad f: An extra channel . So if they hear a breath and they don't know who breath it is it 's better to put it in that channel than to put it in the speaker 's channel because maybe it was someone else 's breath , or  , so I think that 's a good you can always clean that up , post - processing . phd g: So a lot of little details , but I think we 're , coming to some kinda closure , on that . So the idea is then , Don can take , Jane 's post - processed channelized version , and , with some scripts , you know , convert that to to a reference for the recognizer and we can , can run these . So when that 's , ready you know , as soon as that 's ready , and as soon as the recognizer is here we can get , twelve hours of force - aligned and recognized data . And , you know , start , working on it , postdoc b: And phd g: so we 're , I dunno a coup a week or two away I would say from , if if that process is automatic once we get your post - process , transcript . I 'm just hoping that the units that are provided in that way , will be sufficient cuz I would save a lot of , time , dividing things . Just from I dunno how long were you did one ? grad e: I saw a couple , around twenty seconds , and that was just without looking too hard for it , so , I would imagine that there might be some that are longer . postdoc b: Well n One question , e w would that be a single speaker or is that multiple speakers overlapping ? grad e: No . No , but if we 're gonna segment it , like if there 's one speaker in there , that says " OK " or something , right in the middle , it 's gonna have a lot of dead time around it , phd g: Right . It 's not the it 's not the fact that we can't process a twenty second segment , it 's the fact that , there 's twenty seconds in which to place one word in the wrong place grad e: so it 's not postdoc b: Yeah . phd g: You know , if if someone has a very short utterance there , and that 's where , we , might wanna have this individual , you know , ha have your pre pre - process input . phd a: I I I thought that perhaps the transcribers could start then from the those mult multi - channel , speech - nonspeech detections , if they would like to . phd g: So that 's probably what will happen , but we 'll try it this way and see . We 'll probably get lots of errors because of the cross - talk , and , noises and things . postdoc b: Oh I wanted to ask one thing , the microphones the new microphones , professor c: Yeah ? K . postdoc b: when do we get ,  ? grad f: they said it would take about a week . phd g: So what happens to our old microphones ? professor c: They go where old microphones go . grad f:  phd g: Do we give them to someone , or ? grad f: Well the only thing we 're gonna have extra , for now , phd g: We don't have more receivers , we just have grad f: Right , we don so the only thing we 'll have extra now is just the lapel . Since , what I decided to do , on Morgan 's suggestion , was just get two , new microphones , and try them out . grad f: Since they 're they 're like two hundred bucks a piece , we won't , at least try them out . phd d: So it 's a replacement for this headset mike ? grad f: Yep . phd d: What 's the , style of the headset ? grad f: It 's , it 's by Crown , and it 's one of these sort of mount around the ear thingies , and , when I s when I mentioned that we thought it was uncomfortable he said it was a common problem with the Sony . grad f: And I checked on the web , and every site I went to , raved about this particular mike . It 's apparently comfortable and stays on the head well , so we 'll see if it 's any good . postdoc b: You said it was used by aerobics instructors ? grad f:  Yep . professor c: For the recor for the record Adam is not a paid employee or a consultant of Crown . professor c: I said " For the record Adam is is not a paid consultant or employee of Crown " . grad f: The P Z Ms are Crown , aren't they ? professor c: Yeah . professor c: So if we go to a workshop about all this this it 's gonna be a meeting about meetings about meetings . professor c: Oh , yeah , what Which 'll be the meeting about the meeting about the meeting . grad f: Cuz then it would be a meeting about the meeting about the meeting about meetings . phd a: S s grad f: Pause between the lines , remember ? grad e: Excuse me