B: Yeah .
B: And if it changes to point five point five , which is equal @ @ for transition and self loop where it becomes eighty - eight percent .
F: Well , but that involves mucking with the back - end ,
F: which is not allowed .
B: Yeah .
B: We can't do it .
F: Yeah .
B: Yeah .
E: Mmm .
B: So .
G: I mean , it uh , like , i i
G: i It is well known , this this medium match condition of the Finnish data has some strange effects .
B: Very s
E: Yeah .
B: It has a very few at uh , actually , c uh , tran I mean , words also .
G: I mean , that is G: Yeah ,
G: that too .
B: It 's a very , very small set , actually .
G: Yeah .
G: Uh - huh .
G: There is a l a There is a lot of Uh , there are a lot of utterances with music in with music in the background .
B: So there is B: Yeah .
B: Yeah , yeah , yeah .
G: Mmm .
B: Yeah .
D: Uh - huh .
B: Yeah .
B: It has some music also .
B: I mean , very horrible music like like
B: I know .
D: So maybe for that one you need a much smarter VAD ?
D: Mmm ,
D: if it 's music .
B: Uh B: So ,
B: that that 's the that 's about the results .
B: And , uh ,
B: the summary is like B: OK .
B: So there are the other thing what I tried was , which I explained in the last meeting , is using the channel zero for , uh , for both dropping and estimating the noise .
B: And that 's like just to f n get a feel of how good it is .
B: I guess the fifty - six percent improvement in the SpeechDat - Car becomes like sixty - seven percent .
B: Like ten percent better .
B: But that 's that 's not a that 's a cheating experiment .
B: So .
B: That 's just B: So ,
B: m
B: w
G: But the but the , uh , forty - seven point nine percent which you have now , that 's already a remarkable improvement in comparison to the first proposal .
B: Yeah .
B: So we had forty - four percent in the first proposal .
G: OK .
G: Mm - hmm .
B: Yeah .
B: We have f a big im
B: So the major improvement that we got was in all the high mismatch cases ,
B: because all those numbers were in sixties and seventies
B: because we never had any noise compensations .
G: Mmm .
B: So that 's where the biggest improvement came up .
B: Not much in the well match and the medium match and TI - digits also right now .
B: So this is still at three or four percent improvement over the first proposal .
G: Mmm .
G: Mmm .
D: Yeah , so that 's good .
B: Yeah .
B: So .
D: Then if we can improve the noise estimation , then it should get better .
G: Yeah ,
G: I I started thinking about also G: I mean yeah , uh , I discovered the same problem when I started working on uh , on this Aurora task almost two years ago ,
G: that you have the problem with this mulit
G: a at the beginning we had only this multi condition training of the TI - digits .
B: Yeah .
G: And , uh , I I found the same problem .
G: Just taking um , what we were used to u use , I mean , uh , some type of spectral subtraction , <comment> y you get even worse results than the basis
B: Yeah .
G: and uh B: Yeah ,
B: yeah .
G: I I tried to find an explanation for it ,
G: so D: Mmm .
B: So .
B: Yes .
B: Stephane also has the same experience of using the spectral subtraction right ?
G: Mmm .
E: Mm - hmm .
B: Yeah .
E: Yeah .
B: So here here I mean , I found that it 's if I changed the noise estimate I could get an improvement .
B: So that 's so it 's something which I can actually pursue , is the noise estimate .
G: Mm - hmm .
B: And G: Yeah , I think what you do is in when when you have the the this multi - condition training mode , um then you have then you can train models for the speech , for the words , as well as for the pauses where you really have all information about the noise available .
B: Yeah .
G: And
G: it was surprising G: At the beginning it was not surprising to me that you get really the best results on doing it this way ,
G: I mean , in comparison to any type of training on clean data and any type of processing .
G: But it was G: So , u u
G: it it seems to be the best what wh wh what what we can do in this moment is multi - condition training .
G: And every when we now start introducing some some noise reduction technique we we introduce also somehow artificial distortions .
B: Yeah .
G: And these artificial distortions uh , I have the feeling that they are the reason why why we have the problems in this multi - condition training .
G: That means the H M Ms we trained , they are they are based on Gaussians ,
B: Yeah .
G: and on modeling Gaussians .
G: And if you G: Can I move a little bit with this ?
G: Yeah .
G: And if we introduce now this this u spectral subtraction , or Wiener filtering stuff G: So , usually what you have is maybe , um G: I 'm I 'm showing now an envelope
G: um
G: maybe you 'll f for this time .
G: So usually you have maybe in clean condition you have something which looks like this .
G: And if it is noisy it is somewhere here .
G: And then you try to subtract it or Wiener filter or whatever .
G: And what you get is you have always these problems , that you have this these these these zeros in there .
B: Yeah .
G: And you have to do something if you get these negative values .
G: I mean , this is your noise estimate and you somehow subtract it or do whatever .
G: Uh , and then you have G: And then I think what you do is you introduce some some artificial distribution in this
G: uh
G: in in the models .
G: I mean , i you you train it also this way
G: but , i somehow there is u u there is no longer a a Gaussian distribution .
G: It is somehow a strange distribution which we introduce with these artificial distortions .
G: And and I was thinking that that might be the reason why you get these problems in the especially in the multi - condition training mode .
D: Mm - hmm .
B: Yeah , yeah .
B: Th - That 's true . Yeah B: the c the models are not complex enough to absorb that additional variability that you 're introducing .
G: s
F: Thanks Adam .
G: Yeah .
G: Yes .
B: Well , that 's B: Yeah .
B: So E: I also have the feeling that um , the reason ye why it doesn't work is yeah , that the models are much are t um , not complex enough .
E: Because I actually I als always had a good experience with spectral subtraction ,
E: just a straight spectral subtraction algorithm when I was using neural networks , big neural networks , which maybe are more able to model strange distributions
G: Mm - hmm .
E: and E: But E: Yeah .
E: Then I tried the same exactly the same spectral subtraction algorithm on these Aurora tasks
E: and it simply doesn't work .
G: Hmm .
E: It 's even it , uh , hurts even .
E: So .
D: We probably should at some point here try the tandem the the the system - two kind of stuff with this , with the spectral subtraction for that reason .
G: Hmm .
G: Mm - hmm .
D: Cuz again , it should do a transformation to a domain where it maybe looks more Gaussian .
E: Mm - hmm .
G: Hmm .
G: Yeah ,
G: y I I was whe w w
G: just yesterday when I was thinking about it um w what what we could try to do , or do about it G: I mean , if you if you get at this in this situation that you get this this negative values and you simply set it to zero or to a constant or whatever if we if we would use there a somehow , um a random generator which which has a certain distribution , u not a certain <comment> yeah , a special distribution we should see we we have to think about it .
B: It 's G: And that we , so , introduce again some natural behavior in this trajectory .
D: Mm - hmm .
B: Mm - hmm .
B: Very different from speech .
B: Still , I mean , it shouldn't confuse the G: Yeah , I mean , similar to what what you see really u in in the real um noisy situation .
B: OK .
B: Mm - hmm .
G: Or i in the clean situation .
G: But but somehow a a natural distribution .
D: But isn't that s again sort of the idea of the additive thing ,
D: if it as as we had in the J stuff ?
D: I mean , basically if  if you have random data , um , in in the time domain , then when you look at the s spectrum it 's gonna be pretty flat .
G: Mm - hmm .
D: And and ,
D: uh ,
D: so just add something everywhere rather than just in those places .
D: It 's just a constant , right ?
E: Mm - hmm .
G: Yeah .
G: I think e
G: yeah .
G: It 's it 's just especially in these segments ,
G: I mean , you introduce , um , very artificial behavior .
D: Yeah .
G: And D: Yeah .
D: Well , see if you add something everywhere , it has almost no effect up up up on on top .
E: Mm - hmm .
D: And it and it and it has significant effect down there .