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false | Well this is in very interesting | QMSum_135 |
false | So. | QMSum_135 |
false | because i it basically has a i it shows very clearly the contrast between , uh , speech recognition research and discourse research because in in discourse and linguistic research , what counts is what 's communit communicative. | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | And breath , you know , everyone breathes , they breathe all the time. And once in a while breath is communicative , but r very rarely. OK , so now , I had a discussion with Chuck about the data structure | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | and the idea is that the transcripts will that get stored as a master there 'll be a master transcript which has in it everything that 's needed for both of these uses. | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | And the one that 's used for speech recognition will be processed via scripts. You know , like , Don 's been writing scripts | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | and and , uh , to process it for the speech recognition side. Discourse side will have this this side over he the we we 'll have a s ch Sorry , not being very fluent here. But , um , this the discourse side will have a script which will stri strip away the things which are non - communicative. OK. So then the then let 's let 's think about the practicalities of how we get to that master copy with reference to breaths. So what I would r r what I would wonder is would it be possible to encode those automatically ? Could we get a breath detector ? | QMSum_135 |
false | Oh , just to save the transcribers time. | QMSum_135 |
false | Well , I mean , you just have no idea. I mean , if you 're getting a breath several times every minute , | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | and just simply the keystrokes it takes to negotiate , to put the boundaries in , to to type it in , i it 's just a huge amount of time. | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | Oops. | QMSum_135 |
false | Wh - what | QMSum_135 |
false | Yeah. | QMSum_135 |
false | And you wanna be sure it 's used , and you wanna be sure it 's done as efficiently as possible , and if it can be done automatically , that would be ideal. | QMSum_135 |
false | what if you put it in but didn't put the boundaries ? | QMSum_135 |
false | Well , but | QMSum_135 |
false | So you just know it 's between these other things , | QMSum_135 |
false | Well , OK. So now there 's there 's another another possibility | QMSum_135 |
false | right ? | QMSum_135 |
false | which is , um , the time boundaries could mark off words from nonwords. And that would be extremely time - effective , if that 's sufficient. | QMSum_135 |
false | Yeah I mean I 'm think if it 's too if it 's too hard for us to annotate the breaths per se , we are gonna be building up models for these things and these things are somewhat self - aligning , so if so , we i i if we say there is some kind of a thing which we call a " breath " or a " breath - in " or " breath - out " , the models will learn that sort of thing. Uh , so but you but you do want them to point them at some region where where the breaths really are. So | QMSum_135 |
false | OK. But that would maybe include a pause as well , | QMSum_135 |
false | Well , there 's a there 's | QMSum_135 |
false | and that wouldn't be a problem to have it , uh , pause plus breath plus laugh plus sneeze ? | QMSum_135 |
false | Yeah , i You know there is there 's this dynamic tension between between marking absolutely everything , as you know , and and and marking just a little bit and counting on the statistical methods. Basically the more we can mark the better. But if there seems to be a lot of effort for a small amount of reward in some area , and this might be one like this Although I I I 'd be interested to h get get input from Liz and Andreas on this to see if they Cuz they 've - they 've got lots of experience with the breaths in in , uh , uh , their transcripts. | QMSum_135 |
false | They have lots of experience with breathing ? | QMSum_135 |
false | Actually Well , yes they do , but we we can handle that without them here. But but but , uh , you were gonna say something about | QMSum_135 |
false | Yeah , I I think , um , one possible way that we could handle it is that , um , you know , as the transcribers are going through , and if they get a hunk of speech that they 're gonna transcribe , u th they 're gonna transcribe it because there 's words in there or whatnot. If there 's a breath in there , they could transcribe that. | QMSum_135 |
false | Yeah. Yeah. | QMSum_135 |
false | That 's what they 've been doing. So , within an overlap segment , they they do this. | QMSum_135 |
false | Right. But Right. But if there 's a big hunk of speech , let 's say on Morgan 's mike where he 's not talking at all , um , don't don't worry about that. | QMSum_135 |
false | Yeah. | QMSum_135 |
false | So what we 're saying is , there 's no guarantee that , um So for the chunks that are transcribed , everything 's transcribed. But outside of those boundaries , there could have been stuff that wasn't transcribed. So you just somebody can't rely on that data and say " that 's perfectly clean data ". Uh do you see what I 'm saying ? | QMSum_135 |
false | Yeah , you 're saying it 's uncharted territory. | QMSum_135 |
false | So I would say don't tell them to transcribe anything that 's outside of a grouping of words. | QMSum_135 |
false | That sounds like a reasonable reasonable compromise. | QMSum_135 |
false | Yeah , and that 's that that quite co corresponds to the way I I try to train the speech - nonspeech detector , as I really try to not to detect those breaths which are not within a speech chunk but with which are just in in a silence region. | QMSum_135 |
false | Yeah. | QMSum_135 |
false | And they so they hopefully won't be marked in in those channel - specific files. | QMSum_135 |
false | u I I wanted to comment a little more just for clarification about this business about the different purposes. | QMSum_135 |
false | But | QMSum_135 |
false | Yeah , so | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | See , in a in a way this is a really key point , that for speech recognition , uh , research , uh , um , e a it 's not just a minor part. In fact , the I think I would say the core thing that we 're trying to do is to recognize the actual , meaningful components in the midst of other things that are not meaningful. So it 's critical it 's not just incidental it 's critical for us to get these other components that are not meaningful. Because that 's what we 're trying to pull the other out of. That 's our problem. If we had nothing | QMSum_135 |
false | Yeah. | QMSum_135 |
false | if we had only linguistically - relevant things if if we only had changes in the spectrum that were associated with words , with different spectral components , and , uh , we we didn't have noise , we didn't have convolutional errors , we didn't have extraneous , uh , behaviors , and so forth , and moving your head and all these sorts of things , then , actually speech recognition i i isn't that bad right now. I mean you can you know it 's it 's the technology 's come along pretty well. | QMSum_135 |
false | Yeah. | QMSum_135 |
false | The the the reason we still complain about it is because is when when you have more realistic conditions then then things fall apart. | QMSum_135 |
false | OK , fair enough. I guess , um , I uh , what I was wondering is what what at what level does the breathing aspect enter into the problem ? Because if it were likely that a PDA would be able to be built which would get rid of the breathing , so it wouldn't even have to be processed at thi at this computational le well , let me see , it 'd have to be computationally processed to get rid of it , but if there were , uh , like likely on the frontier , a good breath extractor then , um , and then you 'd have to | QMSum_135 |
false | But that 's a research question , you know ? And so | QMSum_135 |
false | Yeah , well , see and that 's what I wouldn't know. | QMSum_135 |
false | that And we don't either. I mean so so the thing is it 's it right now it 's just raw d it 's just data that we 're collecting , and so we don't wanna presuppose that people will be able to get rid of particular degradations because that 's actually the research that we 're trying to feed. So , you know , an and maybe maybe in five years it 'll work really well , | QMSum_135 |
false | OK. | QMSum_135 |
false | and and it 'll only mess - up ten percent of the time , but then we would still want to account for that ten percent , so. | QMSum_135 |
false | I guess there 's another aspect which is that as we 've improved our microphone technique , we have a lot less breath in the in the more recent , uh , recordings , so it 's in a way it 's an artifact that there 's so much on the on the earlier ones. | QMSum_135 |
false | Uh - huh. I see. | QMSum_135 |
false | One of the um , just to add to this one of the ways that we will be able to get rid of breath is by having models for them. I mean , that 's what a lot of people do nowadays. | QMSum_135 |
false | Right. | QMSum_135 |
false | Right. | QMSum_135 |
false | Yeah. | QMSum_135 |
false | And so in order to build the model you need to have some amount of it marked , so that you know where the boundaries are. | QMSum_135 |
false | Hmm. | QMSum_135 |
false | Yeah. | QMSum_135 |
false | So I mean , I don't think we need to worry a lot about breaths that are happening outside of a , you know , conversation. We don't have to go and search for them to to mark them at all , but , I mean , if they 're there while they 're transcribing some hunk of words , I 'd say put them in if possible. | QMSum_135 |
false | OK , and it 's also the fact that they differ a lot from one channel to the other because of the way the microphone 's adjusted. | QMSum_135 |
false | Yeah. | QMSum_135 |
false | Mm - hmm. | QMSum_135 |
false | OK. | QMSum_135 |
false | Should we do the digits ? | QMSum_135 |
false | Yep. OK. | QMSum_135 |
false | OK. | QMSum_135 |
false | Mmm. Alright. | QMSum_135 |
false | Hmm. | QMSum_121 |
false | Okay. Good morning everybody. Um I'm glad you could all come. I'm really excited to start this team. Um I'm just gonna have a little PowerPoint presentation for us , for our kick-off meeting. My name is Rose Lindgren. I I'll be the Project Manager. Um our agenda today is we are gonna do a little opening and then I'm gonna talk a little bit about the project , then we'll move into acquaintance such as getting to know each other a little bit , including a tool training exercise. And then we'll move into the project plan , do a little discussion and close , since we only have twenty five minutes. First of all our project aim. Um we are creating a new remote control which we have three goals about , it needs to be original , trendy and user-friendly. I'm hoping that we can all work together to achieve all three of those. Um so we're gonna divide us up into three compa three parts. First the functional design which will be uh first we'll do individual work , come into a meeting , the conceptional design , individual work and a meeting , and then the detailed design , individual work and a meeting. So that we'll each be doing our own ideas and then coming together and um collaborating. Okay , we're gonna get to know each other a little bit. So um , what we're gonna do is start off with um let's start off with Amina. Um Alima , | QMSum_121 |
false | Alima. | QMSum_121 |
false | sorry , Alima. | QMSum_121 |
false | Um we're gonna do a little tool training , so we are gonna work with that whiteboard behind you. Um introduce yourself , um say one thing about yourself and then draw your favourite animal and tell us about it. | QMSum_121 |
false | Okay. Um I don't know which one of these I have to bring with me. | QMSum_121 |
false | Probably both. | QMSum_121 |
false | Right , so , I'm supposed to draw my favourite animal. I have no drawing skills whatsoever. But uh let's see , introduce myself. My name is Alima Bucciantini. Um I'm from the state of Maine in the US. I'm doing nationalism studies , blah , blah , blah , and I have no artistic talents. | QMSum_121 |
false | How do you spell your name ? | QMSum_121 |
false | A_ L_ I_ M_ A_. | QMSum_121 |
false | Thanks. | QMSum_121 |
false | Oh , and I guess I'm the Industrial Designer on this project. So let's see if I can get | QMSum_121 |
false | um here. I will draw a little turtle for you all. Not necessarily 'cause it's my absolute favourite animal , but just that I think they're drawable. And you have the pretty little shell going on. Some little eyes. Happy. There you go. That's a turtle. | QMSum_121 |
false | Yes. | QMSum_121 |
false | So what are your favourite characteristics ? | QMSum_121 |
false | Um. I I like the whole having a shell thing. | QMSum_121 |
false | Mm. | QMSum_121 |
false | It's quite cool carry your home around where you go , um quite decorative little animals , they can swim , they can , they're very adaptable , they carry everything they need with them , um and they're easy to draw. | QMSum_121 |
false | Excellent. | QMSum_121 |
false | Shall we just go around the table ? | QMSum_121 |
false | Uh Okay. Well , my name is Iain uh | QMSum_121 |
false | Mm. | QMSum_121 |
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