but um we just didn't have time to play with you know tuning yet another yet another parameter.


the level.


yeah.


and really the reason we can't do it is just that we don't have a we don't have ground truth for these.


so we would need a hand marked um word level alignments.


or at least sort of the boundaries of the speech you know between the speakers.


um and then use that as a reference.


and tune the parameters of the of the model uh to to get the best performance.


given i i mean i i i was going to ask you anyway uh how you assessed that things were better?


uhhuh.


i looked at them.


i spent two days um in waves.


okay.


oh it was painful.


because the thing is you know the alignments share a lot in common.


so


and you're you're looking at these segments where there's a lot of speech.


i mean a lot of them have a lot of words.


not by every speaker.


but by some speaker there's a lot of words.


yeah.


no not


i mean that if you look at the individual segments from just one person you don't see a lot of words.


but altogether you'll see a lot of words up there.


and so the reject is also mapping and pauses.


so i looked at them all in waves.


and just lined up all the alignments.


and at first it sort of looked like a mess.


and then the more i looked at it i thought okay well it's moving these words leftward.


and you know it wasn't that bad.


it was just doing certain things wrong.


but i don't you know have time to to look at all of them.


and it would be really useful to have like a a transcriber who could use waves.


um just mark like the beginning and end of the foreground speaker's real words.


like the beginning of the first word the end of the last word.


and then we could you know do some adjustments.


i okay.


i have to ask you something.


because if we could benefit from what you did incorporate that into the present transcripts that would help.


no.


and then um the other thing is i believe that i did


so one of these transcripts was gone over by a transcriber.


and then i hand marked it myself so that we do have uh the beginning and ending of individual utterances.


uhhuh.


um i didn't do it word level.


but but in terms


so i so for for one of the n.s.a. groups.


and also i went back to the original one that i first transcribed and and did it uh uh utterance by utterance for that particular one.


so i think you do have


if that's a sufficient unit i think that you do have hand marking for that.


but it'd be wonderful to be able to benefit from your waves stuff.


we don't care what what tool you use.


yeah i mean if if you can um if you want to


okay.


i used it in transcriber.


uh


and it's it's in the


well jane and i were just in terms of the tool talking about this.


i guess sue had had some reactions.


you know interface wise if you're looking at speech you want to be able to know really where the words are.


yeah that's right.


and so we can give you some examples of sort of what this output looks like.


middle of the word or


um and see if you can maybe incorporate it into the transcriber tool some way.


or


well i i'm thinking just incorporating it into the representation.


um


i mean if it's if it's


you mean like yeah word start insights.


if you have start points if you have like time tags


right.


which is what i assume.


isn't that what what you


well see adam would be


yeah whatever you use.


i mean we convert it to this format that the um nist scoring tool uh c.t.m. conversation time marked file.


yeah.


and and then that's the that's what the


i think transcriber uh outputs c.t.m.


if it


okay.


i think so.


so you would know this more than i would.


so i mean


it seems like she if she's if she's moving time marks around


since our representation in transcriber uses time marks it seems like there should be some way of of using that benefitting from that.


yeah it the advantage would just be that when you brought up a bin you would be able if you were zoomed in enough in transcriber to see all the words.


uhhuh.


you would be able to like have the words sort of located in time.


if you wanted to do that.


so so if we even just had a a it sounds like we we almost do.


so


uh if we we have two.


we have two.


yeah.


just uh trying out the alignment procedure that you have on that.


you could actually get something um uh uh get an objective measure.


uh


you mean on on the hand marked um


so we we only i only looked at actually alignments from one meeting that we chose.


i think m.r. four.


just randomly um


and


actually not randomly.


not randomly.


we knew we knew that it had these insertion errors from.


it had sort of average recognition performance in a bunch of speakers.


and it was a meeting recorder meeting.


um


but yeah we should try to use what you have.


i did rerun recognition on your new version of m.r. one.


oh good.


i i mean the the one with dan ellis in it and eric.


good.


uhhuh yeah exactly yeah yeah.


i don't think that was the new version.


that


yeah actually it wasn't the new new.


okay.


it was the medium new.


but but we would we should do the the latest version.


yeah.


you did you adjust the the utterance times um for each channel?


it was the one from last week.


yes.


yes i did.


and furthermore i found that there were a certain number where not not a lot but several times i actually moved an utterance from adam's channel to dan's or from dan's to adam's.


so there was some speaker


and the reason was because i transcribed that at a point before uh before we had the multiple audio available.


so i couldn't switch between the audio.


i i transcribed it off of the mixed channel entirely.


which meant in overlaps i was at a at a terrific disadvantage.


right.


in addition it was before the channelized uh possibility was there.


and finally i did it using the speakers of my um of you know off the c.p.u. on my on my machine.


because i didn't have a headphone.


so it like i mean


yeah i i mean in retrospect it would've been good to have i should've gotten a headphone.


but in any case um this is this was transcribed in a in a uh less optimal way than than the ones that came after it.


and i was able to you know and this meant that there were some speaker identifications.


well i know there were some speaker labelling problems um after interruptions.


which were changes


yeah.


fixed that.


is that what you're referring to?


i mean because there's this one instance when for example you're running down the stairs.


oh well


i remember this meeting really well.


don don has had he knows he can just read it like a play.


it's a


yeah i've i've i'm very well acquainted with this meeting.


yeah i can


and then she said and then he said.


yeah i know it by heart.


so um there's one point when you're running down the stairs.


uhoh!


right?


and like there's an interruption.


you interrupt somebody.


but then there's no line after that.


for example there's no speaker identification after that line.


uhhuh.


is that what you're talking about?


or were there mislabelings as far as like the adam was?


that was fixed um before.


i think i think i understood that pretty


yeah because i thought i let you know about that.


thank you for mentioning.


yeah.


yeah no that that i think went away a couple of versions ago.


okay.


but you're actually saying that certain uh speakers were mis identified.


but it's good to know.


yeah so with under um uh listening to the mixed channel there were times when as surprising as that is i got adam's voice confused with dan's and vice versa.


not for long utterances.


but just a couple of places.


and embedded in overlaps.


the other thing that was interesting to me was that i picked up a lot of um backchannels which were hidden in the mixed signal.


which you know i mean you not not too surprising.


right.


but the other thing that


i i hadn't thought about this.


but i i wanted to raise this when you were uh with respect to also a strategy which might help with the alignments potentially.


but that's


when i was looking at these backchannels they were turning up usually very often in well i won't say usually but anyway very often i picked them up in a channel which was the person who had asked a question.


so like someone says and have you done the so-and-so.


and then there would be backchannels.


but it would be the person who asked the question.


other people weren't really doing much backchanneling.


and you know sometimes you have the yeah uhhuh.


well that's interesting.


i mean it wouldn't be perfect.


yeah.


but but it does seem more natural to give a backchannel when when you're somehow involved in the topic.


no that's really interesting.


and the most natural way is for you to have initiated the topic by asking a question.


uhhuh.


well


i think


that's interesting.


no i think it's actually i think what's going on is backchanneling is something that happens in two party conversations.


and if you ask someone a question you essentially initiating a little two party conversation.


well yeah when we looked at this.


so then you're so and then you're expected to backchannel.


exactly.


because the person is addressing you directly and not everybody.


exactly my point.


and so this is the expectation thing that uh uh


right.


just the dyadic


but in addition you know if someone has done this analysis himself and isn't involved in the dyad but they might also give backchannels to verify what what the answer is that this that the the answerer's given


i tell you i say i say uhhuh a lot.


it's


there you go.


well but it's interesting.


because uh


while people are talking to each other.


but there are fewer i think there are fewer uhhuh.


yeah.


i mean just from we were looking at word frequency lists to try to find the cases that we would allow to have the reject words in between in doing the alignment.


you know the ones we wouldn't constrain to be next to the other words.


oh yeah.


and uhhuh is not as frequent.


as it sort of would be in switchboard.


if you looked at just a word frequency list of one word short utterances.


and yeah is way up there.


but not uhhuh.


and so i was thinking


it's not like you're being encouraged by everybody else to keep talking in the meeting.


and uh that's all i'll stop there.


because i think what you say makes a lot of sense.


well that's right.


and that would


well


but it was sort of


and what you say is the is the uh other side of this.


which is that you know so there are lots of channels where you don't have these backchannels when a question has been asked.


and and these


right.


there's just probably less backchanneling in general.


uhhuh.


so that's good news really.


even if you consider every other person altogether one person in the meeting.


but we'll find out anyway.


we were i guess the other thing we're we're i should say is that we're going to um try compare this type of overlap analysis to switchboard.


and callhome.


where


where we have both sides so that we can try to answer this question of you know is there really more overlap in meetings or is it just because we don't have the other channel in switchboard?


and we don't know what people are doing.


try to create a paper out of that.


yeah i mean you folks have probably already told me.


but were were you intending to do a eurospeech submission?


or


um you mean the one due tomorrow?


yeah.


yeah well we're still like writing the scripts for doing the research.


and we will yes we're going to try.


uhhuh.


and i was telling don do not take this as an example of how people should work.


do as i say.


that's


so we will try.


don't do as i do.


well


it'll probably be a little late.


it is different.


but i'm going to try it.


in previous years eurospeech only had the abstract due by now.


not the full paper.


right.


and so all our timing was off.


i've given up on trying to do digits.


i just don't think that what i have so far makes a eurospeech paper.


well i'm we may be in the same position.


and i figured we'll try.


because that'll at least get us to the point where we have we have this really nice database format that andreas and i were working out that


it it's not very fancy.


it's just a ascii line by line format.


but it does give you information.


it's the it's the spurt format.


it yeah we're calling these spurts after chafe.


i was trying to find what's a word for a continuous region with pauses around it.


huh.


yeah i know that the telecom people use use spurt for that.


good.


they do?


oh.


yes.


and that's i mean i i was using that for a while when i was doing the rate of speech stuff.


i would


because i because i looked up in some books and i found okay i want to find a spurt in which


uh right.


it's just like defined by the acoustics.


and because because it's another question about how many pauses they put in between them.


horrible.


right.


but how fast do they do the words within the spurt?


yeah.


you know burst also?


it's going to


well that's what we were calling spurt.


burst.


isn't burst is used also?


so


spurt has the horrible name overloading with other with hardware at icsi.


here.


just very locally yeah.


but but that just


well well chafe had this i think it was chafe or somebody had a the word spurt originally.


here


and so i


actually


but that's good to know.


was it's chafe?


so maybe we should talk


well see i know sue wrote about spurts of development.


maybe it was sue


but in any case i think it's a good term.


huh!


and uh


so we have spurts and we have spurt-ify dot shell and spurt-ify.


yeah.


and maybe maybe chafe did.


uh


so


i know i know chafe dealt with


and then it's got all it's a verb now.


that's cool.


chafe speaks about intonation units.


yes right.


but maybe he speaks about spurts as well.


and i just don't know.


yeah go ahead.


so what we're doing


i've heard burst also.


uh this this is just maybe someone has some some ideas about how to do it better.


huh.


but we so we're taking these uh alignments from the individual channels.


we're


from each alignment we're producing uh one of these c.t.m. files.


which essentially has it's just a linear sequence of words with the begin times for every word and the duration.


great.


and and and of course


it looks like a waves label file almost right?


right but it has one the first column has the meeting name.


it's just


so it could actually contain several meetings.


um


and the second column is the channel.


third column is the um start times of the words and the fourth column is the duration of the words.


and then we're


okay then we have a messy alignment process where we actually insert into the sequence of words the uh tags.


for like where where sentence ends of sentence.


question marks.


um various other things.


yeah these are things that we had don


uh


so don sort of um propagated the punctuation from the original transcriber.


right.


so whether it was like question mark or period or um you know comma and things like that.


and we kept the disfluency dashes uh kept those in because we sort of want to know where those are relative to the spurt overlaps.


uhhuh.


so so those are actually sort of retro fitted into the time alignment.


overlaps.


or


and then we merge all the alignments from the various channels.


and we sort them by time.


and then there's a then there's a process where you now determine the spurts.


that is actually no you do that before you merge the various channels.


so you you identify by some criterion.


which is pause length.


you identify the beginnings and ends of these spurts.


and you put another set of tags in there to keep those straight.


and then you merge everything in terms of you know linearizing the sequence based on the time marks.


and then you extract the individual channels again.


but this time you know where the other people start and end talking.


you know where their spurts start and end.


and so you extract the individual channels uh one spurt by spurt as it were.


um and inside the words or between the words you now have begin and end tags for overlaps.


so you you basically have everything sort of lined up and in a form where you can look at the individual speakers and how their speech relates to the other speakers' speech.


right.


uh i mean i think that's actually really useful also.


and


because even if you weren't studying overlaps if you want to get a transcription for the far field mikes how are you going to know which words from which speakers occurred at which times relative to each other?


you have to be able to get a transcript like like this anyway just for doing far field recognition.


so


you know it's it's sort of


yeah.


i it's just an issue we haven't dealt with before.


how you time align things that are overlapping anyway.


that's wonderful.


well


and and we


i mean i never thought about it before.


yes.


in


but


i mean when i came up with the original data suggested data format based on the transcription graph there's capability of doing that sort of thing in there.


right.


uhhuh.


but you can't get it directly from the transcription.


yeah that's right.


well this is this is just


yeah this is like a poor man's formatting version.


but it's you know it's clean.


it's just not fancy.


well there's lots of little things.


um


it's like there're twelve different scripts which you run.


and then at the end you have what you want.


but um


at the very last stage we throw away the actual time information.


all we care about is whether that there's a certain word was overlapped by someone else's word.


so you sort of at that point you discretize things into just having overlap or no overlap.


because we figure that's about the level of analysis that we want to do for this paper.


but if you wanted to do a more fine-grained analysis and say you know how far into the word is the overlap you could do that.


it's just it'll just require more


yeah.


just sort of huge.


you know slightly different.


what's interesting is it's exactly what um in discussing with um sue about this.


um she um indicated that that you know that's very important for overlap analysis.


yeah it's it's nice to know.


right.


and also i think as a human like i don't always hear these in the actual order that they occur.


so i can have two foreground speakers.


you know morgan and um adam and jane could all be talking.


and i could align each of them to be starting their utterance at the correct time.


and then look where they are relative to each other.


and that's not really what i heard.


and that's another thing she said.


because it's just hard to do.


this is this is bever's bever's effect.


when where in psycholinguistics you have these experiments where people have perceptual biases as to what they hear.


yeah.


it's sort of


that that not the best


yeah you sort of move things around until you get to a low information point.


and then you can bring in the other person.


so it's actually not even possible i think for any person to listen to a mixed signal.


even equalize and make sure that they have all the words in the right order.


uhhuh.


so i guess we'll try to write this eurospeech paper.


superb.


i mean we will write it.


whether they accept it late or not i don't know.


um and the good thing is that we have it's sort of a beginning of what don can use to link the prosodic features from each file to each other.


yeah that's the good thing about these


plus


so


huh?


you know might as well.


i otherwise we won't get the work done on our deadline.


i don't know


i mean jane likes to look at data.


yeah.


maybe you know you could you could look at this format and see if you find anything interesting.


i don't know.


well what i'm thinking is


no it's that's the good thing about these paper deadlines and uh you know class projects and and things like that.


uhhuh.


right.


well my


well the other thing that that that that you usually don't tell your graduate students is that these deadlines are actually not that um you know strictly enforced.


because you you really get


forces you to do the work.


exactly.


strict.


because the


oh now it's out in the public.


this this this secret information.


because


i think we can


yeah.


nah


no.


so


nah.


because these the conference organizers actually have an interest in getting lots of submissions.


right.


i mean a a monetary interest.


so um


that's that's true.


and good ones.


good ones.


and good


which sometimes means a little extra time.


that's


well


that's true.


that's another issue.


but


by by the way this is totally unfair you may you may feel.


but the the uh the morning meeting folks actually have an an extra month or so.


uhhuh.


yep.


yep the aurora there's a special aurora


when


uh


there's a special aurora session.


and the aurora people involved in aurora have till uh early may or something to turn in their paper.


oh.


huh.


well then you can just


oh well maybe we'll submit to actually


maybe you can submit the digits paper on for the aurora session.


yeah.


oh i could!


if it


i could submit that to aurora.


well


that would be pretty pretty


it has


that wouldn't work.


no it wouldn't work.


it's not aurora.


it's it's not the aurora


i mean it it's it's actually the aurora task.


maybe they'll get


aurora's very specific.


well maybe it won't be after this deadline extension.


but but the people i mean


a a paper that is not on aurora would probably be more interesting at that point.


maybe they'll


because everybody's so sick and tired of the aurora task.


yeah.


oh i thought you meant this was just the digits section.


i didn't know you meant it was aurora digits.


well no if you if you have it's to if you discuss some relation to the aurora task.


like if you use the same


this is not the aurora task.


so they just do a little grep for


do uh


um


do not do not we are not setting a good example.


well a relation other than negation maybe.


this is not a


so


i don't know.


anyway


but the good thing is this does


well i don't know.


i mean you could you could do a paper on what's wrong with the aurora task by comparing it to other ways of doing it.


how well does an aurora system do on on you know on digits collected in a in this environment?


different way.


yeah.


maybe.


pretty hokey.


i think it's a little farfetched.


nah i mean the thing is aurora's pretty closed community.


yep.


i mean you know the people who were involved in the the only people who are allowed to test on that are people who who made it above a certain threshold in the first round.


uhhuh.


it's very specific.


uh in ninety nine.


and it's it's sort of a it's


well that's maybe why they don't know that they have a crummy system.


not like a


i mean a crummy back end.


no i mean i mean seriously.


if you if you have a very


no i'm sorry.


uh beep!


no i didn't mean anybody any particular system.


i mean


i meant this h.t.k. back end.


if they


oh you don't like h.t. k?


yeah.


i don't i don't have any stock in h.t.k. or entropic or anything.


no i mean this it's the h.t.k. that is trained on a very limited amount of data.


it's it's very specific.


right.


but so if you but maybe you should you know consider more using more data.


or i mean


oh yeah i i really think that that's true.


if if you sort of hermetically stay within one task and don't look left and right then you're going to


and they


but they they had


but


they had something very specific in mind when they designed it right?


well


and so so you can you can argue about maybe that wasn't the right thing to do.


but you know they they they had something specific.


but one of the reasons i have chuck's messing around with with the back end that you're not supposed to touch


uhhuh.


i mean for the evaluations yes we'll run a version that hasn't been touched.


but uh one of the reasons i have him messing around with that because i think it's sort of an open question that we don't know the answer to.


people always say very glibly that if you show improvement on a bad system that doesn't mean anything.


because it may not be show uh because you know it doesn't tell you anything about the good system.


and i i've always sort of felt that that depends.


you know that if some if you're actually are getting at something that has some conceptual substance to it it will port.


and in fact most methods that people now use were originally tried with something that was not their absolute best system at some level.


but of course sometimes it doesn't uh port.


so i think that's that's an interesting question.


if we're getting three percent error on uh uh english uh native speakers um using the aurora system and we do some improvements and bring it from three to two do those same improvements bring uh you know the s.r.i. system from one point three to you know to point eight


huh.


zero.


well


you know so that's that's something we can test.


right.


so


anyway


okay.


i think we've we've covered that one up extremely well.


uhhuh.


whew!


so um


yeah.


so so we'll you know maybe you guys will have have one.


uh you you and uh and dan have have a paper that that's going in.


you know that's that's pretty solid on the segmentation stuff.


i will send you the the final version.


and the aurora folks here will will definitely get something in on aurora.


which is not


actually this this um so there's another paper.


it's a eurospeech paper but not related to meetings.


but it's on digits.


uh a colleague at s.r.i. developed a improved version of m.m.i.e. training.


and he tested it mostly on digits.


because it's sort of a you know it doesn't take weeks to train it.


right.


um


and got some very impressive results um with you know discriminative uh gaussian training um you know like um error rates go from i don't know in very noisy environment like from


uh uh


i now i okay now i have the order of


i'm not sure about the order of magnitude.


was it like from ten percent to eight percent or from you know point you know from one percent to point eight percent?


it got it got better.


i mean it's a


it got better.


yeah.


that's the important thing.


yeah yeah.


hey that's the same percent relative.


but it's


so


yeah right.


it's uh something in


right.


twenty percent relative gain.


um let's see.


i think the only thing we had left was unless somebody else


well there's a couple things.


uh one is anything that um anybody has to say about saturday?


anything we should do in prep for saturday?


um


i guess everybody knows about


i mean um mari was asking was trying to come up with something like an agenda.


and we're sort of fitting around people's times a bit.


but um clearly when we actually get here we'll move things around this as we need to.


but


okay.


so you can't absolutely count on it.


yeah.


but but uh


are we meeting in here probably?


or


that was my thought.


i think this is


are we recording it?


we won't have enough microphones.


no i i hadn't intended to.


there's no way.


we we want to i mean they're there's going to be uh jeff katrin mari and two students.


so there's five from there.


and brian.


but you know


and brian's coming.


so that's six.


uhhuh.


and plus all of us.


can use the oprah mike.


uh


depends how fast you can throw it.


it seems like too many too much coming and going.


it's just


yeah.


well


we don't even have enough channel


because it would be a different kind of meeting.


that's what i'm


but


i hadn't really thought of it.


maybe just maybe not the whole day.


but just you know maybe some i mean


maybe part of it.


part of it?


make everyone read digits.


at the same time.


please.


we


i don't know.


that's their initiation into our


into our our our cult.


yeah our yeah our


maybe the sections that are not right you know after lunch when everybody's still munching.


and


okay.


so can you send out a schedule once you know it


well


okay yeah i guess i sent it around a little bit.


is is there a


there's a


but


is it changed now or


i hadn't heard back from mari after i i uh brought up the point about andreas's schedule.


so um maybe when i get back there'll be some some mail from her.


so i'll make a


i'm looking forward to seeing your representation.


that'd be uh


and we should get the two meetings from


i'd like to see that.


yeah.


i mean i know about the first meeting.


um


but the other one that you did.


the n.s.a. one.


which we hadn't done because we weren't running recognition on it.


uhhuh.


because the non native speaker


there were five non native speakers.


i see.


but it would be useful for the to see what we get with that one.


great.


so


okay.


it's uh two thousand eleven twenty one one thousand?


yeah three.


right.


i sent email when i finished the that one.


n.s.a. three i think.


that was sort of


yeah that's right that's right.


that's much simpler.


i don't know what they said.


but i know the number.


that part's definitely going to confuse somebody who looks at these later.


um


i mean this is we're recording secret n.s.a. meetings.


not the


i mean it's


yeah.


uh


yeah not that n.s.a.


the the


it's network services and applications.


they are hard to understand.


wait.


they're very uh out there.


the


i have no idea what they're talking about.


the um


the other good thing about the alignments is that um it's not always the machine's fault if it doesn't work.


so you can actually find um


it's the person's fault.


problem uh


it's morgan's fault.


you can find


you can find uh problems with with the transcripts.


it's always morgan's fault.


um you know


oh.


and go back and fix them.


yeah.


but


there are some cases like where the the wrong speaker uh these not a lot but where the the wrong person the the speech is attached to the wrong speaker.


and you can tell that when you run it.


or at least you can get clues to it.


interesting.


i guess it does


so these are from the early transcriptions that people did on the mixed signals like what you have.


uhhuh.


it also raises the possibility of um using that kind of representation i mean i don't know this would be something we'd want to check but maybe using that representation for data entry.


and then displaying it on the channelized uh representation.


because it i think that the i mean my my preference in terms of like looking at the data is to see it in this kind of musical score format.


and also you know sue's preference as well.


yeah if you can get it to


and and


but i mean this if this is a better interface for making these kinds of uh you know local changes then that'd be fine too.


i don't i have no idea.


i think this is something that would need to be checked.


yeah.


okay.


the other thing i had actually was


i i didn't realize this till today.


but uh this is uh jose's last day.


is my last my last day.


oh!


my my last meeting about meetings.


you're not going to be here tomorrow?


oh that's right.


tomorrow


because uh i leave uh the next sunday.


the last meeting meeting?


it's off.


i will come back to home to spain.


uhhuh.


oh.


yeah.


i so i i


and i i would like to to to say thank you very much uh to all people in the group and at icsi.


it was good having you.


huh.


because i i enjoyed very much.


uh


and i'm sorry by the result of overlapping.


because uh i haven't good results uh yet.


but uh i i pretend to to continuing out to spain uh during the the following months.


uh because i have uh another ideas.


but uh i haven't enough time to to with six months it's not enough to to to research.


yep.


and i mean if uh the topic is uh so difficult.


uh in my opinion there isn't


yeah maybe somebody else will come along and will be uh interested in working on it.


and could start off from where you are also you know.


they'd make use of of what you've done.


yeah.


but uh i i will try to recommend uh at uh the spanish government.


but uh the following scholarship uh uh uh will be here more time.


because uh in my opinion is is better uh for us to to spend more time here.


and to work more time in a topic.


no?


yeah it's a very short time.


but uh


yeah six months is hard.


yeah it is.


i think a year is a lot better.


it's difficult.


you you have uh you are lucky.


and you you find a solution in in in some few uh months uh?


okay.


but uh i think it's not uh common.


but uh anyway thank you.


thank you very much.


uh i i bring the chocolate uh to to tear uh with with you.


huh.


uh.


oh.


nice.


i i hope if you need uh something uh from us in the future i i will be at spain to you help.


uh


great.


well


thank you jose.


thank you.


right.


and thank you very much.


have a good trip.


yeah.


keep in touch.


okay.


i guess uh unless somebody has something else we'll read read our digits.


digits?


and we'll get our


are we going to do them simultaneously?


oops.


get our last bit of uh jose's jose jose's digit.


or


you uh


uh i'm sorry?


you prefer uh to eat uh chocolate uh at the coffee break uh at the or you prefer now before after


well we have a time


no we prefer to keep it for ourselves.


during


yeah yeah.


well we have a a time time constraint.


during digits.


so keep it away from that end of the table.


yeah.


why is it that i can read your mind?


well we've got to wait until after after we take the mikes off.


no no.


so are we going to do digits simultaneously?


well


you this is our reward if we do our


or what?


okay.


simultaneous digit chocolate task.


i i think uh it's enough uh for more for more people after.


we're going to we're going to do digits at the same


oh!


huh.


but uh


that's nice.


uhhuh.


um


oh thanks jose.


wow.


to andreas the idea is is good to eat here.


oh.


very nice.


that's that looks great.


oh wow.


oh yeah.


it doesn't it won't leave this room.


all right so in the interest of getting to the


we could do digits while other people eat.


yeah.


so it's background crunching.


huh.


is uh a another acoustic event.


nice.


we don't have background chewing.


background crunch yeah.


no we don't have any data with background eating.


i'm serious.


she's she's serious.


it's just the rest of the digits the rest of the digits are very clean.


i am serious.


she is serious.


are you


oh they're clean.


well


um without a lot of background noise.


yeah.


and it


so i'm just not sure.


you have to write down like while what you're what chocolate you're eating.


because they might make different sounds.


like nuts chocolate with nuts chocolate without nuts.


oh.


crunchy frogs.


um


chocolate adaptation.


actually actually kind of careful.


because i have a strong allergy to nuts.


so i have to sort of figure out one without


that oh yeah they they might.


it's hard to hard to say.


maybe those?


they're so


i don't know.


this is you know this is a different kind of speech.


well


take take several.


looking at chocolates deciding


huh.


you know it's another style.


yeah i may i may hold off.


but if i was uh but maybe i'll get some later.


thanks.


well well why don't we


he he's worried about a ticket.


why don't we do a simultaneous one?


okay.


simultaneous one?


remember to read the transcript number please.


and you laughed at me too the first time i said that.


right.


oops.


i have to what?


yeah.


you laughed at me too the first time i said.


i did.


and now i love it so much.


you really shouldn't uh


okay everyone ready?


you have to sort of um jose if you haven't done this you have to plug your ears while you're talking.


wait wait a minute wait a minute.


we want we want


so that you don't get confused i guess.


we want it synchronized.


hey you've done this before haven't you?


oh you've done this one before?


that's


you've read digits together with us haven't you?


together.


i mean at the same time.


no.


oh you haven't.


i'm not we we oh and you haven't done this either.


okay.


oh okay.


oh yeah.


the first time is traumatic.


yeah.


but


oh and the groupings are important.


huh.


so you're supposed to pause between the groupings.


the grouping.


so uh


you mean that the the grouping is supposed to be synchronized?


yeah sure.


no no.


no?


synchronized digits.


no.


that'd be good.


we'll give everybody the same sheet.


it's like a like a greek like a greek choir?


but they say different


you know?


like


yes.


hey what a good idea.


yeah.


we could do the same sheet for everyone.


have them all read them at once.


well different digits.


uh


but same groupings.


or or just same digits.


so they would all be


see if anyone notices.


yeah that'd be good.


there's so many possibilities.


and then then we can sing them next time.


uh okay why don't we go?


uh one two three go.


okay.


and andreas has the last word.


did you read it twice or what?


he's no he's trying to get good recognition performance.


he had the


yeah.


he had the the long form.


no.


and we're off.


so we're on.


and somewhere is my agenda.


i think the most important thing is morgan wanted to talk about uh the arpa demo.


well so here's the thing.


um why don't we again start off with with


uh uh


yeah i'll get it.


i'll get the door.


um i think we want to start off with the agenda.


and then given that uh liz and andreas are going to be ten fifteen minutes late we can try to figure out what we can do most effectively without them here.


so so so one thing is yeah talk about demo.


okay.


so uh.


uh i b m transcription status.


i b m transcription.


uh what else?


smartkom.


what's smartkom?


uh we want to talk about if if we want to add the data to the meeting recorder corpus.


the data.


the data which we are collecting here.


what what what are we collecting here?


data.


so why don't we have that on the agenda and we'll we'll get to it and talk about it?


the smartkom data.


yeah right.


yeah.


uh right.


uh.


uh reorganization status.


reorganization status.


oh.


files and directories?


files and directories.


yep.


uhhuh.


absinthe which is the multiprocessor unix linux.


i think it was andreas wanted to talk about segmentation and recognition.


and update on s r i recognition experiments.


um


and then if if there's time i wanted to talk about digits.


but it looked like we were pretty full so i can wait till next week.


right.


okay.


well let's see.


i think the certainly the segmentation and recognition we want to maybe focus on when andreas is here since that was particularly his thing.


and also the smartkom thing should


smartkom also andreas.


absinthe i think also he has sort of been involved in a lot of those things.


at least


yeah.


he'll he'll probably be interested.


but


so i mean i think they'll be i'll be interested in all this.


but but uh probably if we had to pick something that we would talk on for ten minutes or so while they're coming here or i guess it would be you think reorganization status?


or


i mean i think chuck was the one who added out the agenda item.


i don't really have anything to say other than that we still haven't done it.


so


well i mean i uh just basically that


maybe i said maybe we said this before just that we met and we talked about it.


and we sort of have a plan for getting things organized.


and


and i and i think a crucial part of that is the idea of of not wanting to do it until right before the next level zero back up so that there won't be huge number of of added.


right.


uh


that that was basically it.


not not much.


although dave basically said that if we want to do it just tell him and he'll do a level zero then.


yeah.


uhhuh.


oh excellent.


oh so maybe we should just go ahead and get everything ready.


oh good.


yep.


so i think we do need to talk a little bit about


well we don't need to do it during this meeting.


we have a little more to discuss.


but uh we're we're basically ready to do it.


and uh i have some web pages on more of the background.


so naming conventions and things like that that i've been trying to keep actually up to date.


so


and i've been sharing them with u u w folks also.


i'm sorry you've been what?


okay.


showing them?


sharing them with the u w folks.


sharing them.


well maybe uh since that that was a pretty short one maybe we should talk about the i b m transcription status.


someone can fill in liz and andreas later.


uh


so we uh we did another version of the beeps where we separated each beeps with a spoken digit.


chuck came up here and recorded some himself speaking some digits.


and so it just goes beep one beep and then the phrase.


and then beep two beep and then the phrase.


and that seems pretty good.


um i think they'll have a easier time keeping track of where they are in the file.


and we have done that on the automatic segmentations.


and we did it with the automatic segmentation.


and i don't think we we didn't look at it in detail.


we just sent it to i b m.


we we sort of spot checked it.


i listened to probably uh five or ten minutes of it from the beginning.


yeah.


oh really?


okay.


and


i sort of spot checked here and there and it sounded pretty good.


so i think it'll work.


and uh we'll just have to see what we get back from them.


uh


and the main thing will be if we can align what they give us with what we sent them.


i mean that's the crucial part.


right.


and i think we'll be able to do that at with this new beep format.


yep.


well i think it's also they are much less likely to have errors.


uhhuh.


i mean so the problem last time is that there were errors in the transcripts where they put beeps where there weren't any or and they put in extraneous beeps.


and with the numbers there it's much less likely.


yeah one interesting note is uh or problem


i don't know if this was just because of how i play it back i say uh s n d play and then the file.


every once in a while uh like a beep sounds like it's cut into two beeps.


yeah.


into two pieces.


yeah and i i don't know if that's an uh artifact of playback.


uh i don't think it's probably in the original file.


um but uh


i recognize that too.


ha.


that's interesting.


i didn't hear that.


but with this new format um that hopefully they're not hearing that and if they are it shouldn't throw them.


yep.


so


well maybe we better listen to it again.


make sure.


but i mean certainly the software shouldn't do that.


that's what i thought.


it's probably just you know huh somehow the audio device gets hung for a second.


uhhuh.


yeah.


some latency or something.


hiccups.


or


as long as they have one number and they know that there's only one beep maximum that goes with that number.


the only the only part that might be confusing is when chuck is reading digits.


right.


well you know actually are we having them


seven four eight beep seven beep eight three two.


yeah but are we having them do digits?


yes.


because uh we don't we didn't in order to cut them out we'd have to listen to it.


we we didn't cut those out.


they are not transcribed yet.


so


and we wanted to avoid doing that.


okay.


so we they are transcribing the digits.


although we could tell them we could tell them if you hear someone reading a digits string just say bracket digit bracket.


we can we can ignore it when we get it back.


huh.


and don't bother actually computing the writing down the digits.


yeah.


that'd be great.


that'd be what i'm having the transcribers here do because it can be extracted later.


yep.


and then i wanted to talk about but as i said i we may not have time what we should do about digits.


we have a whole pile of digits that haven't been transcribed.


let's talk about it.


because that's that's something that i i know andreas is less interested in than liz is.


so you know.


do we have anything else to say about transcription?


it's good.


about i b m stuff?


uh brian i i sent bresset sent brian a message about the meeting.


and i haven't heard back yet.


so i hope he got it and hopefully he's


okay.


huh.


maybe he's gone.


i don't know.


he didn't even reply to my message.


so i should probably ping him just to make sure that he got it.


all right.


so we have a whole bunch of digits.


if we want to move on to digits.


actually maybe i one one more related thing in transcription.


so that's the i b m stuff.


we've got that sorted out.


um how're we doing on the on the rest of it?


we're doing well.


i i hire i've hired two extra people already expect to hire two more.


and um i've prepared um uh a set of five which i'm which i'm calling set two.


which are now being edited by my head transcriber in terms of spelling errors and all that.


she's also checking through and and and monitoring um the transcription of another transcriber.


you know i mean she's going through and doing these kinds of checks.


uhhuh.


and i've moved on now to what i'm calling set three.


i sort of thought if i do it in sets groups of five then i can have like sort of a a parallel processing through through the the current.


and and you indicated to me that we have a a goal now for the for the um the uh darpa demo of twenty hours.


so i'm going to go up to twenty hours be sure that everything gets processed and released and and that's that's what my goal is.


package of twenty hours right now and then once that's done move on to the next.


yeah uh


so twenty hours.


but i guess the other thing is that um that that's kind of twenty hours a. sap because the longer before the demo we actually have the twenty hours the more time it'll be for people to actually do cool things with it.


so okay.


good.


i'm i'm hiring people who uh really are


they would like to do it full time several of these people.


and and i don't think it's possible really to do this full time but that what it shows is motivation to do as many hours as possible.


it'll keep your accuracy up.


yep.


yeah.


and they're really excellent.


well that's good.


yeah i mean i guess the so the difference if if um if the i b m stuff works out the difference in the job would be that they primarily would be checking through things that were already done by someone else?


got a good core group now.


again.


uhhuh.


is that most of what it


and correcting.


i mean correcting.


correcting.


we'll we'll expect that they'll have to move some time bins and do some corrections.


and i you know i've also uh discovered


so with the new transcriber i'm


um


so


uh let me say that my


uh


so um


at present um the people have been doing these transcriptions a channel at a time.


and that sort of um is useful.


and you know and then once in a while they'll have to refer to the other channels to clear something up.


okay.


well i realize that um we're using the pre segmented version.


and um the pre segmented version is extremely useful.


and wouldn't it be useful also to have the visual representation of those segments?


and so i've uh i uh uh i've trained the new one uh the the newest one to um use the visual from the channel that is going to be transcribed at any given time.


and that's just amazingly helpful.


because what happens then is you scan across the signal and once in a while you'll find a blip that didn't show up in the pre segmentation.


oh right.


i see what you mean.


and that'll be something like it's it's interesting.


a backchannel or


once in a while it's a backchannel.


yep.


sometimes it seems to be um similar to the ones that are being picked up.


uhhuh.


and they're rare events but you can really go through a meeting very quickly.


you just you just you know you you scroll from screen to screen looking for blips.


and i think that we're going to end up with uh better coverage of the backchannels.


yeah.


but at the same time we're benefitting tremendously from the pre segmentation.


because there are huge places where there is just absolutely no activity at all.


and uh the audio quality is so good.


so they can they can um scroll through that pretty quick?


that's great.


so i think that that's going to also uh you know speed the efficiency of this part of the process.


huh.


okay.


uh yeah.


so uh


so let's talk about the digits since they're not here yet.


uh so we have a whole bunch of digits that we've read and we have the forms and so on.


um but only a small number of that


well not a small number.


only a subset of that has been transcribed.


and so we need to decide what we want to do.


and uh liz and andreas actually they're not here but they did say at one point that they thought they could do a pretty good job of just doing a forced alignment.


and again i don't think we'll be able to do with that alone because um sometimes people correct themselves and things like that.


but


so i was just wondering what people thought about how automated can we make the process of finding where the people read the digits doing a forced alignment and doing the timing.


well forced alignment would be one thing.


what about just actually doing recognition?


well we we know what they read because we have the forms.


no they make mistakes.


right.


but the point is that we want to get a set of clean digits.


you're talking about as a pre processing step.


right morgan?


um


is that what you're


yeah i'm i'm not quite sure what i'm talking about.


i mean i


i mean uh we're talking about digits now.


and and so um there's a bunch of stuff that hasn't been marked yet.


uh.


and um there's the issue that that they we know what what was said but do we?


i mean so one option


because people make mistakes and stuff.


i was just asking just out of curiosity if if with uh uh the s r i recognizer getting one percent word error.


uh would we would we do better?


so if you do a forced alignment but the but the but the transcription you have is wrong because they actually made mistakes uh or false starts it's it's much less it's much less common than one percent?


but that's pretty uncommon.


um if we could really get one percent on


well i guess yeah i guess if we segmented it we could get one percent on digits.


we should be able to.


right?


yeah.


so that's just my question.


but


i'm not saying it should be one way or the other but it's if


well there there're a couple different ways of doing it.


we could use the tools i've already developed and transcribe it.


hire some people or use the transcribers to do it.


we could let i b m transcribe it.


you know they're doing it anyway.


and unless we tell them different they're going to transcribe it.


um or we could try some automated methods.


and my my tendency right now is well if i b m comes back with this meeting and the transcript is good just let them do it.


well


yeah it's you raised a point kind of uh euphemistically.


but i mean maybe it is a serious problem.


what will they do when they go hear beep seven beep seven three five two?


i mean you think they'll we'll get


it's pretty distinct.


the beeps are pre recorded.


yeah.


it'll only be a problem for for mine.


well it it well it'd be preceded by i'm reading transcript so-and-so?


yes.


so i think if they're processing it at


i mean it'll be it will be in the midst of a digit string.


so


i mean it


sure there there might be a place where it's beep seven beep eight beep eight beep.


but you know they they're they're going to macros for inserting the beep marks.


and so i i don't think it'll be a problem.


we'll have to see.


but i don't think it's going to be a problem.


okay.


well i i i don't know.


i i think that that's if they are in fact going to transcribe these things uh certainly any process that we'd have to correct them or whatever is needs to be much less elaborate for digits than for other stuff.


right.


so why not?


sure.


that was it?


that was it.


just what do we do with digits.


we have so many of them and it'd be nice to actually do something with them.


well we we we want to have them.


you mean there're more than ten?


yeah i


anything else?


your mike is a little low there.


in berkeley yeah.


so uh you you have to go a little early.


right?


well i can stay till about uh three forty.


at twenty


all right.


so let's make sure we do the ones that that uh saved you.


yeah.


uhhuh.


so there was some uh in in adam's agenda list.


he had something from you about segmentation this last recognition?


well yeah.


so this is just partly to inform everybody um and and of course to get um input.


oops!


um so uh we had a discussion don and liz and i had discussion last week about how to proceed with uh you know with don's work.


and and and uh one of the obvious things that occur to us was that we're since we now have thilo's segmenter and it works you know amazingly well um we should actually basically re evaluate the recognition um results using you know without cheating on the segmentations.


and that should be fairly


so


and how do we find the transcripts for those so that


oh okay.


the references for for those segments?


so there's actually


it's not that


why do you ask?


i could


no actually um nist has um a fairly sophisticated scoring program that you can give a um a time.


well


hand ones.


okay.


uh you know you basically just give two time marked sequences of words and it computes the um the uh you know the the


it does all the work for you.


yeah.


so it we just and we use that actually in hub five to do the scoring.


um so what we've been using so far was sort of a simplified version of the scoring.


and we can we can handle the the the type of problem we have here.


so we


so basically you give some time constraints for for the references and for for the hypothesis.


right.


and


maybe the start of your speech and the end of it.


yeah okay.


so


or stuff like that.


it does time constrained word alignment.


so that should be possible.


i mean that shouldn't be a problem.


uh so that was the one thing.


and the other was that


um


what was the other problem?


oh!


that thilo wanted to use the recognizer alignments to train up his um speech detector.


yeah.


um so that we could use uh you know there wouldn't be so much hand labelling needed to uh to generate training data for for the speech detector.


i'm just in progress of of doing that.


and i think you're in the process of doing that.


so you can you can


it'll give you a lot more data too.


won't it?


so it's basically


i think eight meetings or something which which i'm using.


and it's before it was twenty minutes of one meeting.


uhhuh.


so should be a little bit better.


right.


that won't be perfect the alignments aren't perfect.


great.


but um it's probably still better to have all this extra data than


but


yeah.


yep.


we'll see that.


okay.


actually i had a question about that.


if you find that you can lower the false alarms that you get where there's no speech that would be useful for us to know.


so um


there were the false alarms.


so right now you get you know false false uh speech regions when it's just like um breath or something like that.


and i'd be interested to know the if you retrain


um


do those actually go down or not?


because of


i'll can make can like make a comparison of of the old system to the to the new one.


yeah just to see if by doing nothing in the modeling of just having that training data what happens.


and then


um another one that we had on adam's agenda that definitely involved you was something about smartkom?


right.


so rob porzel


uh porzel?


and the uh porzel and the uh smartkom group are collecting some dialogues.


porzel.


basically they have one person sitting in here looking at a picture and a wizard sitting in another room somewhere.


and uh they're doing a travel task.


and uh it involves starting i believe starting with a


it's it's always the wizard.


but it starts where the wizard is pretending to be a computer and it goes through a uh speech generation system.


yeah.


actually it's changed to a synthesis for for the first part now.


synthesis system.


um and then it goes to a real wizard and they're evaluating that.


and they wanted to use this equipment and so the question came up is


well here's some more data.


should this be part of the corpus or not?


and my attitude was yes because there might be people who are using this corpus for acoustics as opposed to just for language.


um or also for dialogue of various sorts.


um so it's not a meeting.


right?


because it's two people and they're not face to face.


wait a minute.


so i just wanted to understand it because i i'm uh hadn't quite followed this process.


um so it's wizard in the usual sense that the person who is asking the questions doesn't know that it's uh a not a machine?


right.


actually actually the the we do this


at the beginning.


i don't know who came up with it but i think it's a really clever idea.


we simulate a computer breakdown halfway through the session and so then after that the person's told that they're now talking to a uh to a human.


yeah.


it's a human operator.


but of course they don't know that it's the same person both times.


so we we collect we collect both human computer and human human data essentially in the same session.


you might want to try collecting it the other way around sometime saying that the computer isn't up yet.


huh.


and then so then you can separate it out whether it's the beginning or end kind of effects.


that's an idea.


but


yep.


that's a good idea.


i have to go now.


you can talk to the computer.


it's a lot more believable too.


no!


if you tell them that they're the computer part is running on a windows machine.


and the whole breakdown thing kind of makes sense.


just just reboot it.


abort


abort retry fail?


so did they actually save the far field data?


because at first they weren't they weren't


well this was this was the question.


yes.


yeah.


so so they were saying they were not going to.


okay.


and i said well that's silly if if we're going to try to do it for a corpus there might be people who are interested in acoustics.


wow!


no.


or


projector we were not saying we are not doing it.


no the the question is do we save one or two far field channels or all of them?


we we just wanted to do


right.


i i see no reason not to do all of them.


that that if we have someone who is doing acoustic studies uh it's nice to have the same for every recording.


um


nnn.


huh.


so what is the purpose of this recording?


uhhuh.


this is to get acoustic and language model training data for smartkom.


it's to be to training data and development data for the smartkom system.


the english system?


yeah.


okay.


where does this


maybe we can have him vary the microphones too.


well


or they're different speakers.


right.


so so so for their usage they don't need anything.


so why not


but but i'm not sure about the legal aspect of of that.


right?


is is there some contract with smartkom or something about the data?


what they


or is is that our data which we are collecting here?


we've never signed anything that said that we couldn't use anything that we did.


or


we weren't supposed to collect any data.


so


okay.


this was all


yeah.


so yeah that was the question.


if if


basically.


no that's not a problem.


i look it seems to me that if we're doing it anyway and we're doing it for these these purposes that we have and we have these distant mikes we definitely should should save it all as long as we've got disk space.


uhhuh.


and disk is pretty cheap.


so should we save it?


and then


now


so we save it because it's it it's potentially useful.


right.


and now what do we do with it is is a separate question.


i mean anybody who's training something up could choose to put it uh to include this or not.


i i would not say it was part of the meetings corpus.


it isn't.


but it's some other data we have.


and if somebody doing experiment wants to train up including that then they can.


right?


so it's it it i guess it the begs the question of what is the meeting corpus.


so if at u w they start recording two person hallway conversations is that part of the meeting corpus?


i think it's i i think i think the idea of two or more people conversing with one another is key.


well this has two or more people conversing with each other.


nnn well


yeah.


they're just not face to face.


what if we just give it a a name like we give these meetings a name?


well this


no it doesn't.


it has


i mean that was my intention.


and then later on some people will consider it a meeting and some people won't.


that was my intention.


and


so so so part of the reason that i wanted to bring this up is do we want to handle it as a special case or do we want to fold it in?


just give it a title.


oh.


i think it is a


give everyone who's involved as their own user i d give it session i ds let all the tools that handle meeting recorder handle it or do we want to special case it?


and if we were going to special case it who's going to do that?


well it it makes sense to handle it with the same infrastructure since we don't want to duplicate things unnecessarily.


so


it it it


i think


but as far as distributing it we shouldn't label it as part of this meeting corpus.


we should let it be its own


yeah.


i don't see why not.


well it's it well because


it's just a different topic.


i i have an extra point which is the naturalness issue.


because we have like meetings that have a reason.


that's one of the reasons that we were talking about this.


and and those and this sounds like it's more of an experimental setup.


it's scenario based it's it's human computer interface it's really pretty different.


it's got a different purpose.


but i i have no problem with somebody folding it in for some experiment they're going to do.


but i don't think it it doesn't match anything that we've described about meetings.


uhhuh.


whereas everything that we talked about them doing at at u w and so forth really does.


okay.


they're actually talking


so so what does that mean for how we are going to organize things?


huh.


yeah.


you can you can


again as as i think andreas was saying if you want to use the same tools and the same conventions there's no problem with that.


it's just that it's you know different directory it's called something different it's


you know


it is different.


you can't just fold it in as if it's


i mean digits are different too.


right?


it might also be potentially confusing.


yeah but those are folded in.


and it's just you just mark the transcripts differently.


so so one option is you fold it in.


right.


and just simply in the file you mark somewhere that this is this type of interaction rather than another type of interaction.


yeah i


well i i wouldn't call reading digits meetings.


i mean we we we were doing


well but but i put it under the same directory tree.


you know it's in user doctor speech data m r.


well


can we just have a directory called like other stuff?


and


other.


well or i don't know.


i mean i don't care what directory tree you have it under.


and and just um store it there.


right?


i mean that's just a


okay.


my preference is to have a single procedure so that i don't have to think too much about things.


yes.


i mean


and just have a marking.


yeah.


if we do it any other way that means that we need a separate procedure and someone has to do that.


o you you can use whatever procedure you want that's convenient for you.


all i'm saying is that there's no way that we're going to tell people that reading digits is meetings.


right.


and similarly we're not going to tell them that someone talking to a computer to get travel information is meetings.


those aren't meetings.


but if it makes it easier for you to fold them in the same procedures and have them under the same directory tree knock yourself out.


you know?


there's a couple other questions that i have too.


and and one of them is what about uh consent issues?


and the other one is what about transcription?


transcription is done in munich.


are


okay.


so we don't have to worry about transcribing it?


all right.


so we will have to worry about format.


that's a that's another argument to keep it separate.


because it's going to follow the smartkom transcription conventions and not the icsi meeting transcription conventions.


oh okay.


yeah.


uh.


well i didn't realize that.


good point.


that's that's a


but i'm sure no one would have a problem with our folding it in for some acoustic modeling or or some things.


um.


do we do we have uh um american born folk uh reading german german uh uh place names and so forth?


is that


exactly.


yep.


yeah great.


they they even have a reading list.


i bet that sounds good.


it's pretty funny.


huh?


you can do that if you want.


yeah.


okay.


i don't know if you want that.


right.


so


huh.


heidelberg.


disk might eventually be an issue so we might we we might need to uh get some more disk pretty soon.


do you want to be a subject?


yeah i be pretty good.


we


we're about we're about half halfway through our disk right now.


that was one of our concerns.


are we only half?


i thought we were more than that.


we're probably a little more than that because we're using up some space that we shouldn't be on.


so once everything gets converted over to the disks we're supposed to be using we'll be probably uh seventy five percent.


well when i was looking for space for thilo i found one disk that had uh i think it was nine gigs and another one had seventeen.


yep.


and everything else was sort of committed.


uh.


were those backed up or non backed up?


those were non backed up.


non back up.


right.


so that's different.


oh you're talking about backed up.


i'm much more concerned about the backed up.


the non backed up.


i haven't looked to see how much of that we have.


yeah.


is cheap.


i mean if we need to we can buy a disk hang it off a uh workstation.


if it's not backed up the sysadmins don't care too much.


so i mean pretty much anytime we need a disk we can get it at the rate that we're


you can


i shouldn't be saying this but you can just you know since the back ups are every night you can recycle the backed up diskspace.


but that's that's that's risky.


yeah you really shouldn't be saying


huh.


i didn't say that.


yeah that's right.


beep that out.


we had allowed dave to listen to these these uh recordings.


right.


um yeah.


i and there's been this conversation going on about getting another file server.


and and we can do that.


uhhuh.


we'll take the opportunity and get another big raft of of disk i guess.


yeah.


well i think i think there's an argument for having you know you could use our old file server for for disks that have data that is very rarely accessed.


it's really the back up issue rather than the file server issue.


and then have a fast new file server for data that is um heavily accessed.


my understanding is the issue isn't really the file server.


we could always put more disks on.


it's the it's the back up


it's the back up system.


so


which is near saturation apparently.


soon.


i think i think the file server could become an issue as we get a whole bunch more new compute machines.


and we've got you know fifty machines trying to access data off of abbott at once.


well we're all right for now.


because the network's so slow.


i mean i think i think we've raised this before and someone said this is not a reliable way to do it.


but the


what about putting the stuff on like c c d rom or d v d or something?


yeah.


that was me.


i was the one who said it was not reliable.


okay.


they they wear out.


oh okay.


the the


but they wear out just from sitting on the shelf?


yep.


absolutely.


or from being read and read?


no.


read and write don't hurt them too much unless you scratch them.


but the the write once and the read writes don't last.


so you don't you don't want to put reproduceable data on them.


uhhuh.


wear out after what amount of time?


year or two.


would it be


year or two?


wow!


huh.


but if that


then you would think you'd hear much more clamoring about data loss.


yeah.


and


i mean yeah all the l


i i don't know many people who do it on c d.


i mean they're the most


l d all the l d c distributions are on c d rom.


they're on c d but they're not that's not the only source.


like


they have them on disk.


and they burn new ones every once in a while.


but you know we have


but if you go if you go


but we have like thirty you know from ten years ago?


no.


we have all sorts of c d roms from a long time ago.


right.


ten years ago.


yeah.


well


okay.


ninety one.


and they're still all fine.


were they burned or were they pressed?


uh both.


i've burned them and they're still okay.


i mean usually they're


the the pressed ones last


well not forever.


they've been finding even those degrade.


oh i see.


but uh the burned ones


i mean when i say two or three years what i'm saying is that i have had disks which are gone in a year.


that's what i


on the average it'll probably be three or four years.


but uh i i you don't want to have your only copy on a media that fails.


huh.


and they do.


so how about


um if you have them professionally pressed you know they're good for decades.


so so how about putting them on that plus like on a on on dat or some other medium that isn't risky?


i think


um


we can already put them on tape.


okay.


and the tape is is very reliable.


uhhuh.


so the the only issue is then if we need access to them.


right.


so that's fine if we don't need access to them.


well if if if you if they last


say they actually last like five years huh in in the typical case.


and and occasionally you might need to re create one.


and then you get your tape out but otherwise you don't.


can't you just


you just put them on


so you just archive it on the tape and then put it on c d as well?


yeah.


oh.


so you're just saying put them on c ds for normal access.


i mean you can do that.


what you


but that's pretty annoying.


because the c ds are so slow.


see


huh.


what'd be nice is a system that re burned the c ds every year.


every time it was a going to going to die.


well i mean the c ds are are an


well


it's like like dynamic d ram.


just before.


just before before it goes bad it burns them in.


the the c d is an alternative to tape.


yeah.


icsi already has a perfectly good tape system and it's more reliable.


you know i would think


so for archiving we'll just use tape.


one one thing i don't understand is if you have the data if if if the meeting data is put on disk exactly once then it's backed up once and the back up system should never have to bother with it uh more than once.


well regardless


well first of all there was um a problem with the archive in that i was every once in a while doing a chmod on all the directories or recursive chmod and chown.


because they weren't getting set correctly every once in a while.


uhhuh.


and i was just doing a minus r star not realizing that that caused it to be re backed up.


uh


but normally you're correct.


but even without that the back up system is becoming saturated.


but but this back up system is smart enough to figure out that something hasn't changed and doesn't need to be backed up again.


sure but we still have enough changed that the nightly back ups are starting to take too long.


the i think the at least the once that you put it on it would it would kill that.


okay.


so so then if so so then let's


it has nothing to do with the meeting.


so


it's just the general icsi back up system is becoming saturated.


right.


so what if we buy uh uh what what do they call these um high density


well why don't you have this have a this conversation with dave johnson rather than with me?


no no.


because this is maybe something that we can do without involving dave and and putting more burden on him.


how about we buy uh uh uh one of these high density tape drives?


and we put the data actually on non backed up disks.


and we do our own back up once and for all all and then and we don't have to bother this up.


actually you know we could do that just with the tape with the current tape.


i don't know what these tapes.


uh


at some point these


i don't know.


what kind of tape drive is it?


is it is


i don't know but it's an automatic robot so it's very convenient.


you just run a program to restore them.


the the one that we have?


yeah.


but it might interfere with their back up schedule.


the i mean


but


no we have we don't we have our own?


uh.


something that doesn't that isn't used by the back up gang?


don't we have something downstairs?


well they


what kind of tape drive?


just in


well


no.


but andreas's point is a good one.


and we don't have to do anything ourselves to do that.


right.


they're already right now on tape.


right?


so your your point is and i think it's a good one that we could just get more disk and put it there.


huh.


on an x h uh x x whatever partition.


yeah.


that's not a bad idea.


yeah that's basically what i was going to say is that a disk is is so cheap it's essentially you know close to free.


so once it's on tape


and the only thing that costs is the back up issue uh to first order.


and we can take care of that by putting it on non back up drives and just backing it up once onto this tape.


uhhuh.


i think that's a good idea.


oh.


okay.


good.


it's good.


so who's going to do these back ups?


the people that collect it?


uh


well i'll talk to dave and and see what how what the best way of doing that is.


it's probably going to


there's a little utility that will manually burn a tape for you.


and that's probably the right way to do it.


yeah and we should probably make that part of the procedure for recording the meetings.


well


yep.


yeah.


that's what i'm wondering.


if


well we're we're going to automate that.


okay.


my intention is to do a script that'll do everything.


i mean you don't have to physically put a tape in the drive?


no.


or


oh okay.


it's all tape robot.


so it's just


so you just sit down at your computer and you type a command.


yeah but then you're effectively using the resources of the back up system.


or is that a different tape robot?


but not at the same time.


but but you would be anyway.


no no.


right?


no no no.


because


see


he's saying get a whole different drive.


yeah just give a


but there's no reason to do that.


well i'm saying is if you go to dave and and and ask him can i use your tape robot.


it we already have it there and it it's


he will say well that's going to screw up our back up operation.


no we won't.


he'll say if if that means that it's not going to be backed up standardly great.


i dave has has promoted this in the past.


so i don't think he's actually against it.


yeah.


it's it's definitely no problem.


oh okay.


all right.


good.


okay.


what about if the times overlap with the normal back up time?


um it's it's just it's just a utility which queues up.


it just queues it up and and when it's available it will copy it.


and then you can tell it to then remove it from the disk or you can you know do it a few days later or whatever you want to do.


after you confirm that it's really backed up.


n w


you saying n w archive?


n w archive.


that's what it is.


yep and if you did that during the day it would never make it to the nightly back ups.


right.


and then there wouldn't be this extra load.


well it if he you have to put the data on a on a non backed up disk to begin with.


so that so that otherwise you don't you


well but you can have it n w archive to you can have uh a non backed up disk n w archived.


and it'll never show up on the nightly back ups.


and then it never


which i'm sure would make the sysadmins very happy.


yeah.


so i think that's a good idea.


okay.


that's what we should do.


so that means we'll probably want to convert all all those files filesystems to non backed up media.


that sounds good.


yep.


um another thing on the agenda said s r i recognition experiments?


what's that?


s r i recognition?


oh.


that wasn't me.


um well


uh.


who's that?


we have lots of them.


uh i don't know.


chuck do you have any any updates?


i'm successfully uh increasing the error rate.


uh


that's good.


huh.


lift the herve approach.


yeah.


so i mean i'm just playing with um the number of gaussians that we use in the the recognizer and


well you have to you have to tell people that you're you're doing you're trying the tandem features.


yes i'm using tandem features.


oh you are?


and i'm still tinkering with the p l p features.


cool.


and


yeah i got confused by the results.


it because uh the meeting before you said okay we got it down to where they're they're within a tenth of a percent.


that was on males.


right.


that was that was before i tried it on the females.


oh.


see women are are trouble.


it's the women are the problem.


okay.


right?


as we all know.


so


well let's just say that men are simple.


so so when so i i had i


that was a quick response.


so we had reached the point where


i'm well rehearsed.


yeah.


we had reached the point where um on the male portion of the development set the um or one of the development sets i should say the um the male error rate with uh icsi p l p features was pretty much identical with uh s r i features.


which are m f c c.


so um then i thought oh great.


i'll i'll just let's make sure everything works on the females.


and the error rate you know there was a three percent difference.


oh.


uhhuh.


is there less training data?


uh


i mean we


no actually there's more training data.


this is on just digits?


no no.


oh sorry.


no.


it's uh


okay.


hub five.


this is on


this is hub five.


oh okay.


yeah.


um and the test data is callhome and switchboard.


so uh so then um


oh and plus the the vocal tract length normalization didn't actually made things worse.


so something's really seriously wrong.


so


um


aha.


so so


so but you see now between between the males and the females there's certainly a much bigger difference in the scaling range than there is say just within the males.


and what you were using before was scaling factors that were just from the the the s r i front end.


and that worked that worked fine.


that's true.


uh but now you're looking over a larger range and it may not be so fine.


well um


i just


so the one thing that i then tried was to put in the low pass filter which we have in the


so most most hub five systems actually band limit the uh at about uh thirty seven hundred um hertz.


uhhuh.


although you know normally i mean the channel goes to four four thousand.


right?


so um


and that actually helped uh uh a little bit.


um and it didn't hurt on the males either.


and i'm now uh trying the


oh and suddenly also the the vocal tract length normalization only in the test on the test data.


so you can do vocal tract length normalization on the test data only or on both the training and the test.


yeah.


and you expect it to help a little bit if you do it only on the test and more if you do it on both training and test.


and so the it now helps if you do it only on the test.


and i'm currently retraining another set of models where it's both in the training and the test.


and then we'll we'll have hopefully even better results.


so


but there's


it looks like there will still be some difference.


maybe between one and two percent um for the females.


huh.


and so um you know i'm open to suggestions.


and it is true that the uh that the you know we are using the


uhhuh.


but


it can't be just the v t l.


because if you don't do v t l in both systems uh you know the the females are considerably worse in the with the p l p features.


no no.


i i remember that.


it's much worse.


yeah.


so there must be some something else going on.


well what's the standard?


yeah so i thought the performance was actually a little better on females than males.


that's what i thought too.


um that overall yes.


but on this particular development test set they're actually a little worse.


but that's beside the point.


we're looking at the discrepancy between the s r i system and the s r i system when trained with icsi features.


right.


i'm just wondering if that if if you have any indication of your standard features.


what's are the


you know if that's also different or in the same direction or not.


you're this is let me ask a more basic


because


uhhuh.


i mean is this uh uh iterative baum welch training?


or is it viterbi training?


or


it's baum welch training.


baum welch training.


and how do you determine when to to stop iterating?


um


well actually we we just basically do a a fixed number of iterations.


huh.


uh in this case four.


um which uh we used to do only three.


and then we found out we can squeeze


and it was basically we're we're keeping it on the safe side.


but you're


right.


it might be that one more iteration would would help but it's sort of


you know.


or maybe or maybe you're doing one too many.


i mean it's it's


no but with baum welch there shouldn't be an over fitting issue really.


uh well there can be.


sure.


well you can try each one on a cross validation set.


it if you if you remember some years ago bill byrne did a thing where he was he was looking at that.


can't you?


and he showed that you could get it.


yeah.


so


but but but um


well yeah.


we can


well that's that's the easy one to check.


because we save all the intermediate models.


do you


and we can


and in each case


what


um i'm sorry.


in each case how do you determine you know the the usual fudge factors?


the uh the uh language uh scaling acoustic scaling.


uh uh


um i i'm actually re optimizing them.


although that hasn't shown to make a big difference.


okay.


and the the question he was asking at one point about pruning.


uh


pruning


remember that one?


pruning in the


well he was he's it looked like the at one point he was looking at the probabilities he was getting out at the likelihoods he was getting out of p l p versus mel cepstrum and they looked pretty different.


yeah the likelihoods were lower for the p l p.


as i recall.


oh.


and so uh there's the question.


you mean did you see this in the s r i system?


uhhuh.


um well the likelihoods are


was just looking through the log files.


and


you can't directly compare them because for every set of models you compute a new normalization.


and so these log probabilities they aren't directly comparable.


because you have a different normalization constants for each model you train.


so


huh.


but still it's a question.


if you have some threshold somewhere in terms of beam search or something?


well yeah.


or


that's what i was wondering.


yeah.


i mean


uh


i mean if you have one threshold that works well because the range of your likelihoods is in this area


we prune very conservatively.


i mean as we saw with the meeting data um we could probably tighten the pruning without really


so we basically we have a very open beam.


but you're only talking about a percent or two.


right?


here we're we're saying that there gee there's this uh there's this difference here.


and it


see because there could be lots of things.


right.


but but but but um let's suppose just for a second that uh we've sort of taken out a lot of the the major differences uh between the two.


course.


uhhuh.


i mean we're already sort of using the mel scale and we're using the same style filter integration.


and and well we're making sure that low and high


actually there is the difference in that.


so for the p l p features we use the triangular filter shapes.


and for the in the s r i front end we use the trapezoidal one.


and what's the top frequency of each?


well now it's the same.


it's thirty thirty to seven hundred and sixty hertz.


yeah.


one's triangular one's trapezoidal.


so


no no.


but


before we with straight p l p it's trapezoidal also.


well


but then we had a slight difference in the in the scale.


uh so


since currently the feacalc program doesn't allow me to change the filter shape independently of the scale.


uhhuh.


and i did the experiment on the s r i front end where i tried the where the standard used to be to use trapezoidal filters.


you can actually continuously vary it between the two.


and so i i i tried the uh triangular ones.


and it did slightly worse but it's really a small difference.


huh.


couple tenths of a percent or something.


okay.


yeah exactly.


so it's not just losing some frequency range.


right.


so it's not


i don't think the filter shape by itself will make a huge difference.


yeah.


so the the other thing that


so


we've always viewed it anyway as the major difference between the two is actually in the smoothing.


uhhuh.


that the that the um p l p and and the reason p l p has been advantageous in uh slightly noisy situations is because p l p does the smoothing at the end by an auto regressive model.


and mel cepstrum does it by just computing the lower cepstral coefficients.


um so um uhhuh.


so one thing i haven't done yet is to actually do all of this with a much larger with our full training set.


so right now we're using a


i don't know.


forty?


it's it's it's a training set that's about um you know by a factor of four smaller than what we use when we train the full system.


so some of these smoothing issues are over fitting for that matter.


and the baum welch should be much less of a factor if you go full whole hog.


could be.


and so so just um so the strategy is to first sort of treat things with fast turnaround on a smaller training set and then when you've sort of narrowed it down you try it on a larger training set.


yeah.


and so we haven't done that yet.


now the other related question though is is uh what's the boot models for these things?


the boot models are trained from scratch.


so we compute


um


so we start with a um alignment that we computed with the sort of the best system we have.


and and then we train from scratch.


so we we do a you know um we collect the uh the observations from those alignments under each of the feature sets that that we train.


and then from there we do


there's a lot of


actually the way it works you first train a phonetically tied mixture model.


you do a total of


first you do a context independent p t m model.


then you switch to a context


you do two iterations of that.


then you do two iterations of of of context dependent phonetically tied mixtures.


and then from that you you do the you you go to a state clustered model.


yeah.


and you do four iterations of that.


so there's a lot of iterations overall between your original boot models and the final models.


i don't think that


huh.


we have never seen big differences.


once i thought oh i can now i have these much better models.


i'll re generate my initial alignments.


then i'll get much better models at the end.


made no difference whatsoever.


it's i think it's uh


right.


the boot models are


well for making things better.


but this for making things worse.


this it


the thought is is is possible another possible partial cause is if the boot models used a used a different feature set that


uhhuh.


but there are no boot models in fact.


you you're not booting from initial models.


you're booting from initial alignments.


which you got from a different feature set.


that's correct.


so those features look at the data differently actually.


yeah but


i mean you know they they will find boundaries a little differently though.


you know all all that sort of thing is actually slightly different.


i'd expect it to be a minor effect.


but but but what i'm what i'm saying is


but


so we for a long time we had used boot alignments that had been trained with a with the same front end but with acoustic models that were like fifteen percent worse than what we use now.


and with a different dictionary with a considerably different dictionary which was much less detailed and much less well suited.


yeah.


and so then we switched to new boot alignments.


which which now had the benefit of all these improvements that we've made over two years in the system.


right.


and the result in the end was no different.


so what i'm saying is the exact nature of these boot alignments is probably not a big factor in the quality of the final models.


yeah maybe not.


but it it i still see it as i mean there's there's a history to this too.


but i uh i don't want to go into.


uhhuh.


but but i i i i think it could be the things that it the data is being viewed in a certain way uh that a beginning is here rather than there and so forth.


because the actual signal processing you're doing is slightly different.


but it's it's that's probably not it.


anyway i i i should really reserve uh any conclusions until we've done it on the large training set um and until we've seen the results with the with the v t l in training.


so


at some point you also might want to take the same thing and try it on uh some broadcast news data or something else that actually has has some noisy noisy components so we can see if any conclusions we come to holds across different data.


uh


and uh with this i have to leave.


huh!


okay.


so is there something quick about absinthe that you


with this said.


uh just what we were talking about before which is that i ported a blass library to absinthe.


and then got got it working with fast forward.


and got a speedup roughly proportional to the number of processors times the clock cycle.


oh.


so that's pretty good.


oh!


cool.


um i'm in the process of doing it for quicknet.


but there's something going wrong.


and it's about half the speed that i was estimating it should be.


and i'm not sure why.


uhhuh.


but i'll keep working on it.


but the what it means is that it's likely that for net training and forward passes we'll absinthe will be a good machine.


especially if we get a few more processors and upgrade the processors.


a few more processors?


how many are you shooting for?


there're five now.


it can hold eight.


oh okay.


yeah we'll just go buy them i guess.


and it's also five fifty megahertz and you can get a gigahertz.


yeah.


so


can you mix uh processors of different speed?


i don't think so.


i think we'd have to do all


okay.


probably just throw away the old ones.


yep.


and


thank you for the box.


and i'll just go buy their process.


huh!


maybe we can stick them in another system.


i don't know.


we'd have to get a almost certainly have to get a uh netfinity server.


i see.


they're pretty pretty specialized.


yeah.


is is liz coming back do you know?


or


oh you don't.


all right.


see you.


um.


so


uh they're having tea out there.


so i guess the other thing that we were going to talk about is is uh demo.


and um so these are the demos for the uh july uh meeting and um darpa


july what?


early july?


late july?


oh i think it's july fifteenth.


sixteen to eighteen i think.


is that it?


yeah sixteenth eighteenth.


roughly.


yeah.


so we talked about getting something together for that.


but maybe uh maybe we'll just put that off for now given that


but i think maybe we should have a a sub meeting.


i think uh probably uh adam and and uh chuck and me should talk about should get together and talk about that sometime soon.


over a cappuccino tomorrow?


yeah something like that.


um uh you know maybe maybe we'll involve dan ellis at some some level as well.


uhhuh.


um.


okay the the tea is is going so uh i suggest we do uh uh a unison.


a unison digits?


okay.


which is going to be a little hard for a couple people because we have different digits forms.


gets our


oops!


we have a i found a couple of old ones.


huh.


oh.


well that'll be interesting.


so


uh


have you done digits before?


no.


i haven't done it.


so uh the idea is just to read each line with a short pause between lines.


all right.


not between.


and uh since we're in a hurry we were just going to read everyone all at once.


so if you sort of plug your ears and read


okay.


so first read the transcript number and then start reading the digits.


sure.


okay?


one two three.


and we're on.


so uh


time?


fourteen whatever.


so topic number one is we got the n s f a t r.


i t r.


excuse me.


which was originally pretty big but it's divided among many sites.


and they like cut the budget by a by two thirds.


so they they approved it for all the sites.


but only gave us like sixty two percent of the money.


or excuse me cut it by sixty two percent.


thirty two.


yeah thirty whatever.


so uh morgan is uh


and the other condition is that they have to get a budget out like immediately.


or or they'll give the money to someone else.


by next friday.


so morgan's working on that.


uhhuh.


so i mean it's good news.


it's not as good as it could have been.


but it was uh


it's more money.


the the i t rs are you know a a long shot and so it's it's really nice that we got that.


what does that stand for?


information technology research.


something like that.


huh.


all right.


it's uh


doesn't say much.


yeah it's pretty vague.


it's it's um it's some money that we can use


it's government run i guess.


uh i mean we're in conjunction with uh s r i washington and um columbia.


uhhuh.


okay.


and to do research on the meetings.


cool.


yeah it was specifically on the meeting.


not great.


so was that the one that was the meeting maps?


yeah.


okay good.


i liked that one.


what is meeting maps?


mapping meetings or


well the the sort of general idea was you can think about meetings at lots of different levels.


all the way from all the way down to acoustics to all the way up to dialogue discourse topic.


and so this this was how do you map all that information onto that?


onto the meeting domain.


it's like creating a a map of a meeting in a sense.


so that the the analogy was you have maps of different things at different resolutions.


and so uh


uh okay.


so this had a lot of different stuff in it.


okay.


it actually doesn't have a whole lot of speech stuff to it.


yeah sure.


yeah that's


it's mostly higher level.


it's


yeah.


higher level yeah.


but nonetheless i think if we


it's a it's a cool idea.


and if we can actually get it get it going that would be neat.


so and uh this was the one also that was


uh


was it with susan ervin tripp?


i don't remember.


it might have been


yeah we had to get some


with who?


she's a linguist on campus.


okay.


anyway.


so other topics i wanted to talk about are um the darpa demo which i guess since morgan isn't here we can't really talk about.


i just wanted to make sure it was ready to go.


yeah.


uh but i guess


i sent


your stuff is all ready.


so it's just a question of did morgan get around to testing it?


yeah and i sent you an email


yeah i got it with the instructions.


with the instructions.


i don't know.


so morgan


i guess morgan's leaving on saturday?


yep.


so until then


i guess we might hear something.


yeah so i guess we we had better be on call for a little while.


but


yeah exactly.


did he ever figure out how to switch between applications like he was wanting to do?


i haven't spoken with him.


yeah.


he did?


switch between applications?


how is he going to do it?


alt tab.


it's just alt tab.


like if you hold down alt and then tap tab you just the just the task manager window comes up.


yeah yeah he


yes that's


i i think


he


right.


i think the problem was that i was saying hit alt tab with the assumption that he knew what i meant.


but he didn't.


so


no he said he tried that but he didn't like it because it


well


uh


it took it out of the um


but it doesn't.


presentation mode.


it doesn't.


well because if you just touch alt


if you just hit it once it'll go back to the previous application.


and the previous application was the powerpoint demo.


which was the display of like you know


oh!


and and all i said was oh just cycle through with alt tab.


not the slide show.


because that's the standard way of doing it in windows.


yeah he didn't


but of course he doesn't use windows.


yeah.


right.


so


he didn't know what i meant.


oh so what do you have to do?


huh.


you have alt tab and


hold down at hold down alt.


and then tap tab several times.


and you'll cycle through all the open applications.


uh okay and then it won't


and then you can come back to your full screen version of your


oh!


and i told them um if he decides to use multiple examples for um the prosody demo to open up separate transcribers.


just so you don't have to load up files during the meeting.


that's a good idea.


that's not a bad idea at all.


so that uh


he's got the memory for it.


so


yeah and since the files are short it's just


yeah they're all really short.


i mean the tcl t k overhead is pretty high.


but it shouldn't matter.


how much memory does he have?


no i didn't have any problems.


i'm sorry?


i don't know.


but it seemed it didn't seem to be a problem at all.


i would imagine you add sixty four to a hundred and twenty eight.


huh.


i think he had one twenty eight.


yeah either one will work i think.


uh


but


it was faster than my p c.


i mean it was


so


wow!


yeah it was fine i think


yeah i mean i i i we kind of walked through it a little bit on his on his laptop.


so are we the only ones who are giving demo or is uh washington going to have a demo too?


i could.