Grad D: Um , We have ASR results from Liz , transcript status from Jane , and disk space and storage formats from Don . Does do we have any prefer preference on which way we wanna we wanna go ?
PhD G: Well I was actually gonna skip the ASR results part , in favor of getting the transcription stuff talked about
Grad D: Mm - hmm .
PhD G: since I think that 's more important to moving forward , but I mean Morgan has this paper copy and if people have questions , um , it 's pretty preliminary in terms of ASR results because we didn't do anything fancy , but I think e just having the results there , and pointing out some main conclusions like it 's not the speaking style that differs , it 's the fact that there 's overlap that causes recognition errors . And then , the fact that it 's almost all insertion errors , which you would expect but you might also think that in the overlapped regions you would get substitutions and so forth , um , leads us to believe that doing a better segmentation , like your channel - based segmentation , or some kind of uh , echo cancellation to get basically back down to the individual speaker utterances would be probably all that we would need to be able to do good recognition on the on the close - talking mikes .
PhD A: So these Grad D: Um , why don't you , if you have a hard copy , why don't you email it to the list .
PhD G: So , that 's about the summary But this is Morgan has this paper .
PhD A: Yeah , yeah .
Grad D: Oh it 's in the paper .
Professor F: Yeah , so it 's the same thing ?
PhD G: I mean he he Professor F: It 's the same thing I mailed to every everybody that w where it was ,
PhD G: it it 's that paper .
Grad D: OK .
PhD G: Yeah , yeah .
Grad D: OK then , it 's already been mailed .
PhD G: So , we basically , um , did a lot of work on that
Professor F: yeah .
PhD G: and it 's Let 's see , th I guess the other neat thing is it shows for sure w that the lapel , you know within speaker is bad .
Grad D: Horrible ?
PhD G: And it 's bad because it picks up the overlapping speech .
PhD A: So , your your ASR results were run on the channels synchronized ,
PhD G: Yes , cuz that 's all that w had been transcribed at the time ,
PhD A: OK . OK . OK .
PhD G: um but as we I mean I wanted to here more about the transcription . If we can get the channel asynchronous or the PhD A: Yeah .
PhD G: the closer t that would be very interesting for us
PhD B: So if PhD G: because we Professor F: Yeah , that 's that 's why I only used the part from use
PhD A: Yeah .
Professor F: which we had uh about uh about the alt over all the channels
PhD A: Yeah .
PhD G: Right . That 's PhD A: Yeah sure . Yeah .
Professor F: or mixed channel
PhD A: Yeah .
Professor F: rather mixed signal .
PhD B: So if there was a segment of speech this long
PhD G: cuz PhD A: Yeah .
PhD B: and oh and someone said " oh , " the whole thing was passed to the recognizer ?
Grad D: And someone said " oh " in the front in the middle .
PhD A: There were several speakers in it , yeah .
PhD G: That 's right . In fact I I pulled out a couple classic examples in case you wanna u use them in your talk of
PhD B: That 's why there 's so many insertion errors ?
Grad C: Mm - hmm .
PhD G: Chuck on the lapel , so Chuck wore the lapel three out of four times .
Grad C: Mmm .
Grad D: I noticed that Chuck was wearing the lapel a lot .
PhD B: Early on , yeah .
PhD G: Um , yeah , and I wore the lapel once , and for me the lapel was OK . I mean I still and I don't know why . I 'm But um ,
Grad D: Probably how you wear it wore it I would guess .
PhD G: for you it was Or who was next to me or something like that .
Grad C: Yeah , where you were sitting probably affected it .
PhD A: Yeah .
PhD G: Right , but when Chuck wore the lapel and Morgan was talking there 're a couple really long utterances where Chuck is saying a few things inside , and it 's picking up all of Morgan 's words pretty well and so the rec you know , there 're error rates because of insertion Insertions aren't bounded , so with a one - word utterance and ten insertions you know you got huge error rate .
Grad D: Uh - huh .
PhD A: Yeah .
PhD G: And that 's that 's where the problems come in . So I this is sort of what we expected , but it 's nice to be able to to show it .
Grad D: Right .
PhD G: And also I just wanted to mention briefly that , um , uh Andreas and I called up Dan Ellis who 's still stuck in Switzerland , and we were gonna ask him if if there 're you know , what 's out there in terms of echo cancellation and things like that . Not that we were gonna do it , but we wanted to know what would need to be done .
Grad D: And he said , " Lots lots lots lots . "
PhD G: And he We 've given him the data we have so far , so these sychronous cases where there are overlap .
PhD A: Yep .
PhD G: And he 's gonna look into trying to run some things that are out there and see how well it can do
PhD B: So PhD G: because right now we 're not able to actually report on recognition in a real paper , like a Eurospeech paper , because it would look sort of premature .
PhD B: So So the idea is that you would take this big hunk where somebody 's only speaking a small amount in it , and then try to figure out where they 're speaking {comment} based on the other peopl
PhD G: Right . Or who 's At any point in time who 's the foreground speaker , who 's the background speaker .
PhD A: So yeah PhD B: I thought we were just gonna move the boundaries in .
PhD A: Yeah , should it PhD G: So .
Grad D: Well that 's with the hand stuff .
PhD G: So there 's like Grad D: But how would you do that automatically ?
PhD G: Well ther there 's PhD A: Uh , I 've actually done some experiments with cross - correlation
PhD B: Right .
PhD A: and it seems to work pretty well to to get rid of those those overlaps ,
Grad D: I mean that that 's the sort of thing that you would do .
Professor F: Mm - hmm .
PhD G: Yeah .
PhD A: yeah .
Grad D: So .
PhD G: Yeah . Exactly , so it 's it 's a PhD B: So why do you want to do echo cancellation ?
PhD G: Um , it would be techniques used from adaptive adaptive echo cancellation which I don't know enough about to talk about .
PhD B: Uh - huh .
Professor F: It just it just to r to remove cross - talk .
PhD G: Um .
Grad C: Yeah .
Professor F: Yeah .
PhD G: But , right , um , and that would be similar to what you 're also trying to do , but using um , you know , more than energy PhD A: Yeah .
PhD G: I I don't know what exactly would go into it .
PhD A: Yeah , sure .
PhD B: So it would be PhD G: So the idea is to basically run this on the whole meeting . and get the locations , which gives you also the time boundaries of the individual speak
PhD B: OK . So do sort of what he 's already what he 's trying to do .
PhD G: Right . Except that there are many techniques for the kinds of cues , um , that you can use to do that .
PhD A: Yeah , in another way ,
PhD B: OK , I s I see .
PhD A: yeah . Yeah .
PhD B: Yeah . I see .
Professor F: Yeah , Dave Dave uh is , um , also gonna be doin usin playing around with echo cancellation for the nearfield farfield stuff ,
PhD G: So .
Professor F: so we 'll be PhD G: And I guess Espen ? This is uh is he here too ?
Professor F: Yeah .
PhD G: May also be working So it would just be ver that 's really the next step because we can't do too much , you know , on term in terms of recognition results knowing that this is a big problem
PhD B: Mm - hmm .
PhD G: um , until we can do that kind of processing . And so , once we have some some of yours ,
PhD A: OK . Yeah I 'm working on it .
PhD G: and @ @ we 'll move on .
PhD B: I think this also ties into one of the things that Jane is gonna talk about too .
Grad D: Um ,
PhD G: OK .
Grad D: I also wanted to say I have done all this chopping up of digits ,
Postdoc E: Mm - hmm . Mm - hmm .
Grad D: so I have some naming conventions that we should try to agree on . So let 's do that off - line ,
PhD G: Oh right .
Grad D: we don't need to do it during the meeting .
PhD G: Yeah .
Grad C: OK .
PhD G: Right . Definitely Grad D: And and I have scripts that will extract it out from " key " files
PhD G: Uh , and Don should Grad D: and and do all the naming automatically ,
PhD G: OK .
Grad C: Alright .
Grad D: so you don't have to do it by hand .
PhD G: Great .
Grad C: You 've compiled the list of , uh , speaker names ?
PhD G: So that that 's it for the Grad D: Mm - hmm .
PhD G: Speakers and OK .
Grad C: Not names , but I Ds .
Grad D: Yep . Yeah , names names in the names to I Ds ,
Grad C: OK .
Grad D: so you
PhD G: Great .
Grad D: and it does all sorts of matches because the way people filled out names is different on every single file so it does a very fuzzy sort of match .
PhD G: Right .
Grad C: Cool .
PhD G: So at this point we can sort of finalize the naming , and so forth ,
Grad C: Mm - hmm .
Grad D: Yep .
PhD G: and we 're gonna basically re rewrite out these waveforms that we did because as you notice in the paper your " M O in one meeting and " M O - two " in another meeting and it 's we just need to standardize the
Grad C: Yeah . That was my fault .
PhD G: um , no it 's it 's Professor F: No , I didn't notice that actually .
PhD G: um , that 's why those comments are s are in there .
Grad C: Yeah . Then disregard it then .
Grad D: Yep . So th I now have a script that you can just say basically look up Morgan ,
PhD G: So Professor F: Yeah .
PhD G: Right . OK .
Grad D: and it will give you his ID .
PhD G: Great , great .
Grad C: OK .
Grad D: So . Um ,
PhD G: Terrific .
Grad D: alright . Do we Don , you had disk space and storage formats . Is that something we need to talk about at the meeting , or should you just talk with Chuck at some other time ?
Grad C: Um , I had some general questions just about the compression algorithms of shortening waveforms and I don't know exactly who to ask . I thought that maybe you would be the the person to talk to . So , is it a lossless compression {comment} when you compress ,
Grad D: Mm - hmm .
Grad C: so Grad D: Entropy coding .
Grad C: It just uses entropy coding ?
Grad D: So .
Grad C: OK . So , I mean , I guess my question would be is I just got this new eighteen gig drive installed . Um , yeah , which is Grad D: And I assume half of it is scratch and half of it is ?
Grad C: I 'm not exactly sure how they partitioned it .
Grad D: Probably , yeah .
Grad C: But um ,
Professor F: That 's typical , huh .
Grad C: yeah , I don't know what 's typical here , but um , it 's local though , so Grad D: That doesn't matter .
Grad C: But Grad D: You can access it from anywhere in ICSI . N Grad C: OK . Alright . How do you do that ?
Professor F: In fact , this is an eighteen gig drive , {comment} or is it a thirty six gig drive with eighteen Grad D: N Grad C: Eighteen .
PhD G: Eigh - eighteen . It was a spare that Dave had around Grad D: Slash N slash machine name , slash X A in all likelihood .
Professor F: Oh OK .
Grad C: Oh I see . OK . Alright , I did know that .
Grad D: Um , so the the only question is how much of it The distinction between scratch and non - scratch is whether it 's backed up or not .
Grad C: Mm - hmm . Right .
Grad D: So what you wanna do is use the scratch for stuff that you can regenerate .
Grad C: OK .
Grad D: So , the stuff that isn't backed up is not a big deal because disks don't crash very frequently ,
Grad C: Right .
Grad D: as long as you can regenerate it .
Grad C: Right . I mean all of this stuff can be regenerated ,
PhD G: Yeah it 's Grad C: it 's just a question Grad D: Then put it all on scratch
PhD G: Well the Grad D: because we 're ICSI is is bottlenecked by backup .
Postdoc E: Mm - hmm , very good point .
PhD G: Yeah .
Grad C: OK .
Grad D: So we wanna put PhD G: Well I 'd leave all the All the transcript stuff shouldn't should be backed up ,
Postdoc E: Mm - hmm .
PhD G: but all the waveform {comment} Sound files should not be backed up ,
Grad C: Yeah , I guess Right .
PhD G: the ones that you write out .
Grad C: OK . So , I mean , I guess th the other question was then , should we shorten them , downsample them , or keep them in their original form ? Um Grad D: It just depends on your tools . I mean , because it 's not backed up and it 's just on scratch , if your sc tools can't take shortened format , I would leave them expanded ,
Grad C: Right .
Grad D: so you don't have to unshorten them every single time you wanna do anything .
Grad C: OK .
PhD G: We can downsample them ,
Grad C: Do you think that 'd be OK ?
PhD G: so .
Grad C: To downsample them ?
PhD G: Yeah . Yeah , we get the same performance .
Grad C: OK .
PhD G: I mean the r the front - end on the SRI recognizer just downsamples them on the fly ,
Grad C: Yeah , I guess the only argument against downsampling is to preserve just the original files in case we want to experiment with different filtering techniques .
PhD G: so So that 's Professor F: I I I 'm sorry PhD G: Yeah , if
Professor F: Yeah , l I mean over all our data , we we want to not downsample .
PhD G: fe You 'd you wanna not . OK .
Professor F: Yeah .
PhD G: So we 're what we 're doing is we 're writing out I mean , this is just a question . We 're writing out these individual segments , that wherever there 's a time boundary from Thilo , or or Jane 's transcribers , you know , we we chop it there .
Professor F: Yeah . Mm - hmm .
PhD G: And the reason is so that we can feed it to the recognizer ,
Professor F: Mm - hmm .
PhD G: and throw out ones that we 're not using and so forth .
Professor F: Yeah .
PhD G: And those are the ones that we 're storing .
Grad D: Yeah , as I said , since that 's it 's regeneratable , what I would do is take downsample it ,
PhD G: So Yeah .
Grad D: and compress it however you 're e the SRI recognizer wants to take it in .
PhD G: Yeah .
Professor F: ye
PhD G: So we can't shorten them ,
Grad C: Right .
PhD G: but we can downsample them .
Professor F: Yeah , I mean yeah , I 'm sorry .
PhD G: So .
Professor F: As yeah , as long as there is a a form that we can come from again , that is not downsampled , {comment} then ,
Grad C: r Yeah .
PhD G: Oh yeah th
Grad C: Yeah those are gonna be kept .
PhD G: Yeah . Yeah . That that 's why we need more disk space
Professor F: uuu
PhD G: cuz we 're basically duplicating the originals , um Professor F: Yeah .
Grad D: Right .
Professor F: Then it 's fine . But for for fu future research we 'll be doing it with different microphone positions and so on
PhD G: Oh yeah .
Grad C: Right .
Grad D: Yep .
PhD G: No . We always have the original long ones .
Professor F: we would like to PhD B: So the SRI front - end won't take a uh an an a large audio file name and then a a list of segments to chop out {comment} from that large audio file ?
Professor F: Yeah .
PhD B: They actually have to be chopped out already ?
PhD G: Um , it 's better if they 're chopped out ,
PhD B: Uh - huh .
PhD G: and and it it will be yeah , y we could probably write something to do that , but it 's actually convenient to have them chopped out cuz you can run them , you know , in different orders . You c you can actually move them around .
Grad D: And that 's the whole point about the naming conventions
PhD G: Uh , you can get rid of
Grad D: is that you could run all the English speaking ,
PhD G: Yeah , it it 's a lot faster .
Grad D: all the native speakers , and all the non - native speakers ,
PhD G: Right . You can grab everything with the word " the " in it ,
Grad D: and all the men , and all the women . Yeah .
PhD G: and it 's That 's a lot quicker than actually trying to access the wavefile each time , find the time boundaries and So in principle , yeah , you could do that ,
PhD B: I don't I don't think that 's really right .
PhD G: but it 's but it 's um Grad D: " That 's just not right , man . " The the point PhD G: These are long These are long Grad D: So so s For example , what if you wanted to run run all the native speakers .
PhD G: You know . This is an hour of speech .
Grad D: Right , so if if you did it that way you would have to generate a program that looks in the database somewhere , extracts out the language , finds the time - marks for that particular one , do it that way . The way they 're doing it , you have that already extracted and it 's embedded in the file name . And so , you know , you just say PhD G: We - yeah that 's so that 's part of it
Grad D: y so you just say you know " asterisk E asterisk dot wave " , and you get what you want .
PhD G: is Right . And the other part is just that once they 're written out it it is a lot faster to to process them .
Grad D: Rather than doing seeks through the file .
PhD G: So . Otherwise , you 're just accessing Grad D: This is all just temporary access , so I don't I think it 's all just It 's fine . You know . Fine to do it however is convenient .
PhD G: Right .
Professor F: I mean it just depends how big the file is . If the file sits in memory you can do extremely fast seeks
PhD G: Right . The other thing is that , believe it or not I mean , we have some Professor F: but .
Grad D: Yeah and they don't . Two gig ?
PhD G: So we 're also looking at these in Waves like for the alignments and so forth . You can't load an hour of speech into X Waves .
Professor F: Yeah .
PhD G: You need to s have these small files , and in fact , even for the Transcriber program Um Grad D: Yes you can .
PhD B: Yeah , you you can give Waves a start and an end time . And middle .
PhD G: Yeah , if you try to load s really long waveform into X Waves , you 'll be waiting there for PhD B: No , I I 'm not suggesting you load a long wave file ,
PhD G: Oh
PhD B: I 'm just saying you give it a start and an end time . And it 'll just go and pull out that section .
Grad D: I th w The transcribers didn't have any problem with that did they Jane ?
Postdoc E: What 's th u w in what respect ?
PhD G: Loading the long PhD A: No , with the Transcriber tool , it 's no problem .
Grad D: They loaded they loaded the long long files into X Waves .
PhD G: It takes a very long ti
PhD A: Yeah just to load a transcription
Postdoc E: In the in Mm - hmm .
PhD A :
PhD G: Right .
PhD A: takes a long time ,
PhD G: It takes a l very long time .
PhD A: but not for the wavefile . The wavefile is there immediately .
Postdoc E: Mm - hmm . Yeah .
Grad D: Are you talking about Transcriber or X Waves ?
PhD G: Huh .
PhD A: Yeah . Oh , I 'm tr talking about Transcriber .
PhD G: Actually , you 're talking about Transcriber , right ?
PhD A: Yeah .
Grad D: Because because i we used X Waves to do the digits .
Postdoc E: It was also true of the digits task which was X Waves .
Grad D: And they were loading the full mixed files then ,
Postdoc E: Yeah . Very quickly .
Grad D: and it didn't seem to be any problem .
Postdoc E: I agree .
PhD G: Huh . Well we we have a problem with that , you know , time - wise on a It - it 's a lot slower to load in a long file ,
Grad D: Hmm . Seemed really fast .
PhD G: and also to check the file , so if you have a transcript , um ,
Grad D: Well regardless , it 's Professor F: Yeah .