Professor F: so OK , that was the real real important stuff . Um , I I I figured maybe wait on the potential goals for the meeting uh until we talk about wh what 's been going on . So , uh , what 's been going on ? Why don't we start start over here .
PhD G: Um . Well , preparation of the French test data actually .
Professor F: OK .
PhD G: So , it means that um , well , it is , uh , a digit French database of microphone speech , downsampled to eight kilohertz and I 've added noise to one part , with the actually the Aurora - two noises . And , @ @ so this is a training part . And then the remaining part , I use for testing and with other kind of noises . So we can So this is almost ready . I 'm preparing the the HTK baseline for this task . And , yeah .
Professor F: OK Uh , So the HTK base lines so this is using mel cepstra and so on , or ? Yeah . OK .
PhD G: Yeah .
Professor F: And again , I guess the p the plan is , uh , to uh then given this What 's the plan again ?
PhD G: The plan with these data ?
Professor F: With So So Does i Just remind me of what what you were going to do with the what what what what 's y You just described what you 've been doing . So if you could remind me of what you 're going to be doing .
PhD G: Yeah .
Professor F: Oh , this is yeah , yeah .
PhD G: Uh , yeah .
Grad C: Tell him about the cube .
PhD G: Well . The cube ? I should tell him about the cube ?
Grad C: Yeah .
Professor F: Oh ! Cube . Yeah .
PhD G: Yeah .
PhD E: Fill in the cube .
PhD G: Uh we actually we want to , mmm , Uh , uh , analyze three dimensions , the feature dimension , the training data dimension , and the test data dimension . Um . Well , what we want to do is first we have number for each uh task . So we have the um , TI - digit task , the Italian task , the French task and the Finnish task .
Professor F: Yeah ?
PhD G: So we have numbers with uh systems I mean I mean neural networks trained on the task data . And then to have systems with neural networks trained on , uh , data from the same language , if possible , with , well , using a more generic database , which is phonetically phonetically balanced , and . Um .
Professor F: So - so we had talked I guess we had talked at one point about maybe , the language ID corpus ?
PhD G: Yeah . So .
Professor F: Is that a possibility for that ?
PhD G: Ye - uh  Yeah , but , uh these corpus , w w there is a CallHome and a CallFriend also , The CallFriend is for language ind identification . Well , anyway , these corpus are all telephone speech . So , um . This could be a  a problem for Why ? Because uh , uh , the the SpeechDat databases are not telephone speech . They are downsampled to eight kilohertz but but they are not uh with telephone bandwidth .
Professor F: Yeah . That 's really funny isn't it ? I mean cuz th this whole thing is for developing new standards for the telephone .
Grad C: Telephone .
Professor F: Yeah .
PhD G: Yeah , but the the idea is to compute the feature before the before sending them to the Well , you don't do not send speech , you send features , computed on th the  the device ,
Professor F: Mm - hmm . Yeah , I know , but the reason PhD G: or Well .
Professor F: Oh I see , so your point is that it 's it 's it 's uh the features are computed locally , and so they aren't necessarily telephone bandwidth , uh or telephone distortions .
PhD G: So you Yeah . Yeah .
PhD A: Did you happen to find out anything about the OGI multilingual database ?
Professor F: Yeah , that 's wh that 's wh that 's what I meant .
PhD G: Yeah , it 's Professor F: I said @ @ , there 's there 's there 's an OGI language ID , not the not the , uh the CallFriend is a is a , uh , LDC w thing , right ?
PhD G: Yea - Yeah , there are also two other databases . One they call the multi - language database , and another one is a twenty - two language , something like that . But it 's also telephone speech .
PhD A: Oh , they are ? OK .
PhD G: Uh . Well , nnn .
Professor F: But I 'm not sure PhD G: So Professor F: I mean , we ' r e e The bandwidth shouldn't be such an issue right ? Because e e this is downsampled and and filtered , right ? So it 's just the fact that it 's not telephone . And there are so many other differences between these different databases . I mean some of this stuff 's recorded in the car , and some of it 's I mean there 's there 's many different acoustic differences . So I 'm not sure if . I mean , unless we 're going to include a bunch of car recordings in the in the training database , I 'm not sure if it 's completely rules it out
PhD G: Yeah .
Professor F: if our if we if our major goal is to have phonetic context and you figure that there 's gonna be a mismatch in acoustic conditions does it make it much worse f to sort of add another mismatch , if you will .
PhD G: Mmm .
Professor F: Uh , i i I I guess the question is how important is it to for us to get multiple languages uh , in there .
PhD G: Yeah , but Mm - hmm . Um . Yeah . Well , actually , for the moment if we w do not want to use these phone databases , we we already have uh English , Spanish and French uh , with microphone speech .
Professor F: Mm - hmm . Yeah .
PhD G: So .
Professor F: So that 's what you 're thinking of using is sort of the multi the equivalent of the multiple ?
PhD G: Well . Yeah , for the multilingual part we were thinking of using these three databases .
Professor F: And for the difference in phonetic context that you ? Provide that .
PhD G: Well , this Uh , actually , these three databases are um generic databases .
PhD E: Yeah .
PhD G: So w f for for uh Italian , which is close to Spanish , French and , i i uh , TI - digits we have both uh , digits training data and also more general training data . So . Mmm .
Professor F: Well , we also have this Broadcast News that we were talking about taking off the disk , which is  is microphone data for for English .
PhD G: Yeah . Yeah , perhaps yeah , there is also TIMIT .
Professor F: Yeah .
PhD G: We could use TIMIT .
Professor F: Right . Yeah , so there 's plenty of stuff around . OK , so anyway , th the basic plan is to , uh , test this cube . Yes .
PhD G: Yeah .
PhD E: To fill in the cube .
Professor F: To fill i fill it in , yeah . OK .
PhD G: Yeah , and perhaps , um  We were thinking that perhaps the cross - language issue is not , uh , so big of a issue . Well , w w we perhaps we should not focus too much on that cross - language stuff . I mean , uh , training training a net on a language and testing a for another language .
Professor F: Uh - huh . But that 's PhD G: Mmm . Perhaps the most important is to have neural networks trained on the target languages . But , uh , with a general database general databases . u So that th Well , the the guy who has to develop an application with one language can use the net trained o on that language , or a generic net ,
Professor F: Uh , depen it depen it depends how you mean " using the net " .
PhD G: but not trained on a Professor F: So , if you 're talking about for producing these discriminative features that we 're talking about you can't do that .
PhD G: Mmm .
Professor F: Because because the what they 're asking for is is a feature set . Right ? And so , uh , we 're the ones who have been weird by by doing this training . But if we say , " No , you have to have a different feature set for each language , " I think this is ver gonna be very bad .
PhD G: Yeah .
Grad C: Oh .
PhD G: You think so .
Grad C: That 's PhD E: Oh .
Professor F: So Oh yeah .
PhD G: Mmm .
Professor F: Yeah . I mean , in principle , I mean conceptually , it 's sort of like they want a re @ @ {comment} well , they want a replacement for mel cepstra .
PhD G: Mmm .
Professor F: So , we say " OK , this is the year two thousand , we 've got something much better than mel cepstra . It 's , you know , gobbledy - gook . " OK ? And so we give them these gobbledy - gook features but these gobbledy - gook features are supposed to be good for any language .
PhD G: Hmm .
Professor F: Cuz you don't know who 's gonna call , and you know , I mean so it 's it 's it 's , uh , uh how do you know what language it is ? Somebody picks up the phone . So thi this is their image . Someone picks up the phone , right ?
PhD G: Well , I {comment} chh Professor F: And and he he picks up the ph
PhD G: Yeah , but the the application is there is a target language for the application .
Professor F: Yeah . y y y
PhD G: So , if a Professor F: Well . But , no but , y you you pick up the phone ,
PhD G: Well .
Professor F: you talk on the phone ,
PhD G: Yeah ?
Professor F: and it sends features out . OK , so the phone doesn't know what a what what your language is .
PhD G: Yeah , if Yeah . If it 's th in the phone , but Professor F: But that 's the image that they have .
PhD G: well , it that that could be th at the server 's side ,
Professor F: It could be ,
PhD G: and , well . Mmm , yeah .
Professor F: but that 's the image they have , right ? So that 's that 's I mean , one could argue all over the place about how things really will be in ten years . But the particular image that the cellular industry has right now is that it 's distributed speech recognition , where the , uh , uh , probabilistic part , and and s semantics and so forth are all on the servers , and you compute features of the uh , on the phone . So that 's that 's what we 're involved in . We might might or might not agree that that 's the way it will be in ten years , but that 's that 's that 's what they 're asking for . So so I think that th th it is an important issue whether it works cross - language . Now , it 's the OGI , uh , folks ' perspective right now that probably that 's not the biggest deal . And that the biggest deal is the , um envir acoustic - environment mismatch . And they may very well be right , but I I was hoping we could just do a test and determine if that was true . If that 's true , we don't need to worry so much . Maybe maybe we have a couple languages in the training set and that gives us enough breadth uh , uh , that that that the rest doesn't matter . Um , the other thing is , uh , this notion of training to uh which I I guess they 're starting to look at up there , {comment} training to something more like articulatory features . Uh , and if you have something that 's just good for distinguishing different articulatory features that should just be good across , you know , a wide range of languages .
PhD G: Yeah .
Professor F: Uh , but Yeah , so I don't th I know unfortunately I don't I see what you 're comi where you 're coming from , I think , but I don't think we can ignore it .
PhD G: So we we really have to do test with a real cross - language . I mean , tr for instance training on English and testing on Italian , or Or we can train or else , uh , can we train a net on , uh , a range of languages and which can include the test the test @ @ the target language ,
Grad C: Test on an unseen .
PhD G: or Professor F: Yeah , so , um , there 's there 's , uh This is complex . So , ultimately , uh , as I was saying , I think it doesn't fit within their image that you switch nets based on language . Now , can you include , uh , the the target language ?
PhD G: Yeah .
Professor F: Um , from a purist 's standpoint it 'd be nice not to because then you can say when because surely someone is going to say at some point , " OK , so you put in the German and the Finnish .
PhD G: Mmm .
Professor F: Uh , now , what do you do , uh , when somebody has Portuguese ? " you know ? Um , and Uh , however , you aren't it isn't actually a constraint in this evaluation . So I would say if it looks like there 's a big difference to put it in , then we 'd make note of it , and then we probably put in the other , because we have so many other problems in trying to get things to work well here that that , you know , it 's not so bad as long as we we note it and say , " Look , we did do this " .
PhD G: Mmm ?
PhD A: And so , ideally , what you 'd wanna do is you 'd wanna run it with and without the target language and the training set for a wide range of languages .
Professor F: Uh . Yeah .
PhD G: Yeah , perhaps . Yeah .
PhD A: And that way you can say , " Well , " you know , " we 're gonna build it for what we think are the most common ones " ,
Professor F: Yeah .
PhD G: Yeah .
PhD A: but if that somebody uses it with a different language , you know , " here 's what 's you 're l here 's what 's likely to happen . "
Professor F: Yeah , cuz the truth is , is that it 's it 's not like there are I mean , al although there are thousands of languages , uh , from uh , uh , the point of view of cellular companies , there aren't .
PhD A: Right .
Professor F: There 's  you know , there 's fifty or something , you know ? So , uh , an and they aren't you know , with the exception of Finnish , which I guess it 's pretty different from most most things . uh , it 's it 's , uh most of them are like at least some of the others . And so , our guess that Spanish is like Italian , and and so on . I guess Finnish is a is is a little bit like Hungarian , supposedly , right ?
PhD A: I don't know anything about Finnish .
Professor F: Or is I think well , I kn oh , well I know that H uh , H I mean , I 'm not a linguist , but I guess Hungarian and Finnish and one of the one of the languages from the former Soviet Union are in this sort of same family .
PhD A: Hmm .
Professor F: But they 're just these , you know , uh countries that are pretty far apart from one another , have I guess , people rode in on horses and brought their PhD A: Hmm .
Professor F: OK .
PhD G: The Yeah .