Professor B: So whose VAD is Is is this a ?
PhD A: Uh , they didn't decide yet . I guess i this was one point of the conference call also , but mmm , so I don't know . Um , but Yeah .
Grad E: Oh .
Professor B: Oh , I I think th that would be good . I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,
PhD A: Yeah .
Professor B: so
PhD A: Yeah .
Professor B: if we can cut down on that maybe we can make some progress .
PhD A: M Yeah .
Grad E: Hmm .
PhD A: But I guess perhaps I don't know w Yeah . Uh , yeah . Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features . So mmm , you need more features . So you really need to put more more in the in in the front - end .
Professor B: Yeah .
PhD A: So i
Professor B: Um ,
PhD A: S
Professor B: sure . But i bu
PhD C: Wait a minute . I I 'm confused .
PhD A: Yeah .
PhD C: Wha - what do you mean ?
PhD A: Yeah , if i
Professor B: So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch . So just just for example . So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous PhD C: Oh , oh . I see .
PhD A: Mm - hmm .
Professor B: if it gets an unambiguous result then you 're definitely in a in a in a voice in a , uh , s region with speech . Uh .
PhD C: So there 's this assumption that the v the voice activity detector can only use the MFCC ?
PhD A: That 's not clear , but this  e
Professor B: Well , for the baseline .
PhD C: Yeah .
Professor B: So so if you use other features then y But it 's just a question of what is your baseline . Right ? What is it that you 're supposed to do better than ?
PhD C: I g Yeah .
Professor B: And so having the baseline be the MFCC 's means that people could choose to pour their ener their effort into trying to do a really good VAD
PhD C: I don't s But they seem like two separate issues .
Professor B: or tryi They 're sort of separate .
PhD C: Right ? I mean Professor B: Unfortunately there 's coupling between them , which is part of what I think Stephane is getting to , is that you can choose your features in such a way as to improve the VAD .
PhD A: Yeah .
Professor B: And you also can choose your features in such a way as to prove improve recognition . They may not be the same thing .
PhD C: But it seems like you should do both .
Professor B: You should do both
PhD C: Right ?
Professor B: and and I I think that this still makes I still think this makes sense as a baseline . It 's just saying , as a baseline , we know PhD A: Mmm .
Professor B: you know , we had the MFCC 's before , lots of people have done voice activity detectors ,
PhD A: Mm - hmm .
Professor B: you might as well pick some voice activity detector and make that the baseline , just like you picked some version of HTK and made that the baseline .
PhD A: Yeah . Right .
Professor B: And then let 's try and make everything better . Um , and if one of the ways you make it better is by having your features be better features for the VAD then that 's so be it .
PhD A: Mm - hmm .
Professor B: But , uh , uh , uh , at least you have a starting point that 's um , cuz i i some of the some of the people didn't have a VAD at all , I guess . Right ? And and
PhD A: Yeah .
Professor B: then they they looked pretty bad and and in fact what they were doing wasn't so bad at all .
PhD A: Mm - hmm . Mm - hmm .
Professor B: But , um .
PhD C: Yeah . It seems like you should try to make your baseline as good as possible . And if it turns out that you can't improve on that , well , I mean , then , you know , nobody wins and you just use MFCC . Right ?
Professor B: Yeah . I mean , it seems like , uh , it should include sort of the current state of the art that you want are trying to improve , and MFCC 's , you know , or PLP or something it seems like reasonable baseline for the features , and anybody doing this task , uh , is gonna have some sort of voice activity detection at some level , in some way . They might use the whole recognizer to do it but rather than a separate thing , but  but they 'll have it on some level . So , um .
PhD C: It seems like whatever they choose they shouldn't , you know , purposefully brain - damage a part of the system to make a worse baseline , or Professor B: Well , I think people just had
PhD C: You know ?
Professor B: it wasn't that they purposely brain - damaged it . I think people hadn't really thought through about the , uh the VAD issue .
PhD C: Mmm .
PhD A: Mm - hmm .
Professor B: And and then when the the the proposals actually came in and half of them had V A Ds and half of them didn't , and the half that did did well and the half that didn't did poorly .
PhD C: Mm - hmm .
Professor B: So it 's PhD A: Mm - hmm . Um .
Professor B: Uh .
PhD A: Yeah . So we 'll see what happen with this . And Yeah . So what happened since , um , last week is well , from OGI , these experiments on putting VAD on the baseline . And these experiments also are using , uh , some kind of noise compensation , so spectral subtraction , and putting on - line normalization , um , just after this . So I think spectral subtraction , LDA filtering , and on - line normalization , so which is similar to the pro proposal - one , but with spectral subtraction in addition , and it seems that on - line normalization doesn't help further when you have spectral subtraction .
PhD C: Is this related to the issue that you brought up a couple of meetings ago with the the musical tones
PhD A: I PhD C: and ?
PhD A: I have no idea , because the issue I brought up was with a very simple spectral subtraction approach ,
PhD C: Mmm .
PhD A: and the one that they use at OGI is one from from the proposed the the the Aurora prop uh , proposals , which might be much better . So , yeah . I asked Sunil for more information about that , but , uh , I don't know yet . Um . And what 's happened here is that we so we have this kind of new , um , reference system which use a nice a a clean downsampling - upsampling , which use a new filter that 's much shorter and which also cuts the frequency below sixty - four hertz ,
Professor B: Right .
PhD A: which was not done on our first proposal .
Professor B: When you say " we have that " , does Sunil have it now , too ,
PhD A: I No .
Professor B: or ?
PhD A: No .
Professor B: OK .
PhD A: Because we 're still testing . So we have the result for , uh , just the features
Professor B: OK .
PhD A: and we are currently testing with putting the neural network in the KLT . Um , it seems to improve on the well - matched case , um , but it 's a little bit worse on the mismatch and highly - mismatched I mean when we put the neural network . And with the current weighting I think it 's sh it will be better because the well - matched case is better . Mmm .
Professor B: But how much worse since the weighting might change how how much worse is it on the other conditions , when you say it 's a little worse ?
PhD A: It 's like , uh , fff , fff {comment}  um , {comment}   ten percent relative . Yeah .
Professor B: OK . Um .
PhD A: Mm - hmm .
Professor B: But it has the , uh the latencies are much shorter . That 's PhD A: Uh - y w when I say it 's worse , it 's not it 's when I I uh , compare proposal - two to proposal - one , so , r uh , y putting neural network compared to n not having any neural network . I mean , this new system is is is better ,
Professor B: Uh - huh .
PhD A: because it has um , this sixty - four hertz cut - off , uh , clean downsampling , and , um what else ? Uh , yeah , a good VAD . We put the good VAD . So . Yeah , I don't know . I I j uh , uh pr
Professor B: But the latencies but you 've got the latency shorter now .
PhD A: Latency is short is Yeah .
Professor B: Yeah .
PhD F: Isn't it
PhD A: And so
Professor B: So it 's better than the system that we had before .
PhD A: Yeah . Mainly because  of the sixty - four hertz and the good VAD .
Professor B: OK .
PhD A: And then I took this system and , mmm , w uh , I p we put the old filters also . So we have this good system , with good VAD , with the short filter and with the long filter , and , um , with the short filter it 's not worse . So well , is it Professor B: OK .
PhD A: it 's in Professor B: So that 's that 's all fine .
PhD A: Yes . Uh Professor B: But what you 're saying is that when you do these So let me try to understand . When when you do these same improvements to proposal - one ,
PhD A: Mm - hmm .
Professor B: that , uh , on the i things are somewhat better , uh , in proposal - two for the well - matched case and somewhat worse for the other two cases .
PhD A: Yeah .
Professor B: So does , uh when you say , uh So The th now that these other things are in there , is it the case maybe that the additions of proposal - two over proposal - one are less im important ?
PhD A: Yeah . Probably , yeah .
Professor B: I get it .
PhD A: Um So , yeah . Uh . Yeah , but it 's a good thing anyway to have shorter delay . Then we tried , um , to do something like proposal - two but having , um , e using also MSG features . So there is this KLT part , which use just the standard features ,
Professor B: Mm - hmm . Right .
PhD A: and then two neura two neural networks .
Professor B: Mm - hmm .
PhD A: Mmm , and it doesn't seem to help . Um , however , we just have one result , which is the Italian mismatch , so . Uh . We have to wait for that to fill the whole table , but