Professor D: Uh , probably the day after they leave , but we 'll have to  we 'll have to stop it the day before {comment} we leave .
PhD A: Yeah , yeah . So
PhD C: Huh .
Professor D: I think tha I think the the meeting is on the thirteenth or something .
PhD A: Yeah , this Tuesday , yeah .
Professor D: And , uh , they , uh Right . And the the , uh , results are due like the day before the meeting or something . So
PhD A: Yeah , probably , well
Professor D: I th I think I I think they are ,
PhD A: Yeah , well
Professor D: yeah . So  um , since we have a bit farther to travel than some of the others , uh , we 'll have to get done a little quicker . But , um , I mean , it 's just tracing down these bugs . I mean , just exactly this sort of thing of , you know , why why these features seem to be behaving differently , uh , in California than in Oregon .
PhD C: Hmm .
Professor D: Might have something to do with electricity shortage . Uh , we didn't we didn't have enough electrons here and Uh , but , um Uh , I think , you know , the main reason for having I mean , it only takes w to run the the two test sets in just in computer time is just a day or so , right ?
PhD A: Yeah ,
Professor D: So
PhD A: it 's very short interval .
Professor D: yeah . So , I think the who the whole reason for having as long as we have , which was like a week and a half , is is because of bugs like that . So Huh So , we 're gonna end up with these same kind of sheets that have the the percentages and so on just for the PhD A: Yeah , so there are two more columns in the sheets ,
Professor D: Oh , I guess it 's the same sheets ,
PhD A: two . Yeah , it 's the same sheets ,
Professor D: yeah , yeah PhD A: yeah .
Professor D: just with the missing columns filled in .
PhD A: Yeah .
Professor D: Yeah . Well , that 'll be good . So , I 'll dis I 'll disregard these numbers . That 's that 's that 's good .
PhD A: So , Hynek will try to push for trying to combine , uh , different things ? Or Hmm ?
Professor D: Uh , well that 's um yeah I mean , I think the question is " Is there is there some advantage ? " I mean , you could just take the best system and say that 's the standard . But the thing is that if different systems are getting at good things , um , a again within the constraint of the resources , if there 's something simple that you can do Now for instance , uh , it 's , I think , very reasonable to have a standard for the terminal 's side and then for the server 's side say , " Here 's a number of things that could be done . " So , um , everything that we did could probably just be added on to what Alcatel did , and i it 'd probably work pretty well with them , too . So , um , uh , that 's one one aspect of it . And then on the terminal 's side , I don't know how much , um , memory and and CPU it takes , but it seems like the filtering Uh , I mean , the VAD stuff they both had , right ? And , um , so and they both had some kind of on - line normalization , right ?
PhD A: Uh , yeah .
Professor D: Of sorts , yeah ? So so , it seems like the main different there is the is the , uh , filtering . And the filtering I think if you can shouldn't take a lot of memory to do that Uh , and I also wouldn't think the CPU , uh , would be much either for that part . So , if you can if you can add those in um then , uh , you can cut the data rate in half .
PhD A: Yeah .
Professor D: So it seems like the right thing to do is to on the on the terminal 's side , take what they did , if it if it does seem to generalize well to German and Danish , uh , take what they did add in a filter , and add in some stuff on the server 's side and and and that 's probably a reasonable standard . Um Uh
PhD A: They are working on this already ? Because yeah , Su - Sunil told me that he was trying already to put some kind of , uh , filtering in the  France Telecom .
Professor D: Yeah , so that 's that 's that 's what That would be ideal would be is that they could , you know , they could actually show that , in fact , a combination of some sort , uh , would work even better than what what any of the systems had . And , um , then it would it would , uh be something to to discuss in the meeting . But , uh , not clear what will go on . Um , I mean , on the one hand , um , sometimes people are just anxious to get a standard out there . I mean , you can always have another standard after that , but this process has gone on for a while on already and and people might just wanna pick something and say , " OK , this is it . " And then , that 's a standard . Uh , standards are always optional . It 's just that , uh , if you disobey them , then you risk not being able to sell your product , or  Uh um And people often work on new standards while an old standard is in place and so on . So it 's not final even if they declared a standard . The other hand , they might just say they just don't know enough yet to to declare a standard . So you you you will be you will become experts on this and know more far more than me about the tha this particular standards process once you you go to this meeting . So , be interested in hearing . So , uh , I 'd be , uh , interested in hearing , uh , your thoughts now I mean you 're almost done . I mean , you 're done in the sense that , um , you may be able to get some new features from Sunil , and we 'll re - run it . Uh , but other than that , you 're you 're basically done , right ? So , uh , I 'm interested in hearing hearing your thoughts about where you think we should go from this .
PhD A: Yeah .
Professor D: I mean , we tried a lot of things in a hurry , and , uh , if we can back off from this now and sort of take our time with something , and not have doing things quickly be quite so much the constraint , what what you think would be the best thing to do .
PhD A: Uh , well Hmm Well , first , uh , to really have a look at at the speech  from these databases because , well , we tried several thing , but we did not really look at what what 's happening , and where is the noise , and
Professor D: OK .
PhD A: Eh
Professor D: It 's a novel idea . Look at the data . OK .
PhD A: Yeah .
Professor D: Or more generally , I guess , what what is causing the degradation .
PhD A: Yeah , yeah . Actually , there is one thing that well Um , generally we we think that most of the errors are within phoneme classes , and so I think it could be interesting to to see if it I don't think it 's still true when we add noise , and so we have I I guess the confusion ma the confusion matrices are very different when when we have noise , and when it 's clean speech . And probably , there is much more between classes errors for noisy speech .
Professor D: Mm - hmm .
PhD A: And so , um Yeah , so perhaps we could have a a large gain , eh , just by looking at improving the , uh , recognition , not of phonemes , but of phoneme classes , simply .
Professor D: Mm - hmm .
PhD A: And which is a s a s a simpler problem , perhaps , but which is perhaps important for noisy speech .
Professor D: The other thing that strikes me , just looking at these numbers is , just taking the best cases , I mean , some of these , of course , even with all of our our wonderful processing , still are horrible kinds of numbers . But just take the best case , the well - matched uh , German case after er well - matched Danish after we PhD A: Mm - hmm .
Professor D: the kind of numbers we 're getting are about eight or nine uh p percent error per digit .
PhD A: Mm - hmm . Yeah .
Professor D: This is obviously not usable ,
PhD A: No .
Professor D: right ?
PhD A: Sure .
Professor D: I mean , if you have ten digits for a phone number {comment} I mean , every now and then you 'll get it right . I mean , it 's it 's , uh , um So , I mean , the other thing is that , uh And and a and and also , um part of what 's nice about this is that this is , uh , um a realistic almost realistic database . I mean , it 's still not people who are really trying to accomplish something , but but , uh , within the artificial setup , it isn't noise artificially added , you know , simulated , uh , additive noise .
PhD A: Mm - hmm .
Professor D: It 's real noise condition . And , um , the the training the training , I guess , is always done on the close talking
PhD A: No , actually actually the well - matched condition is still quite di still quite difficult .
Professor D: No ?
PhD A: I mean , it 's they have all these data from the close mike and from the distant mike , from different driving condition , open window , closed window ,
Professor D: Yeah .
PhD A: and they take all of this and they take seventy percent , I think , for training and thirty percent for testing .
PhD E: Mm - hmm .
PhD A: So , training is done on different conditions and different microphones , and testing also is done on different microphone and conditions . So , probably if we only take the close microphones , I guess the results should be much much better than this .
Professor D: I see .
PhD A: Mmm .
Professor D: Oh , OK ,
PhD A: Uh
Professor D: that explains it partially . Wha - what about i in so the the PhD A: Yeah , so there is this , the mismatched is , um the same kind of thing ,
Professor D: go ahead .
PhD A: but the driving conditions , I mean the speed and the kind of road , is different for training and testing , is that right ?
PhD E: Yeah .
PhD A: And the last condition is close microphone for training and distant for testing . Yeah .
Professor D: Uh , OK ,
PhD A: So  s so Professor D: so I see . So , yeah , so the high so the right so the highly mismatched case is in some sense a good model for what we 've been , you know , typically talking about when we talk about additive noise in And so and i i k it does correspond to a realistic situation in the sense that , um , people might really be trying to , uh , call out telephone numbers or some or something like that , in in their cars
PhD A: Yeah .
Professor D: and they 're trying to connect to something .
PhD A: Mmm .
Professor D: Um
PhD A: Actually , yeah , it 's very close to clean speech training because , well , because the close microphone and noisy speech testing ,
Professor D: Yeah . Yeah .
PhD A: yeah . Mmm .
Professor D: Yeah . And the well - matched condition is what you might imagine that you might be able to approach , if you know that this is the application . You 're gonna record a bunch on people in cars and so forth , and do these training . And then , uh , when y you sell it to somebody , they will be a different person with a different car , and so on . So it 's this is a an optim somewhat optimistic view on it , uh , so , you know , the real thing is somewhere in between the two .
PhD A: Yeah .
Professor D: Uh , uh , but
PhD A: But the I mean , the th th
Professor D: Even the optimistic one is
PhD A: it doesn't work .
Professor D: Yeah ,