C: Yeah .
C: There 's an uh , I think it 's still for even for the evaluation ,
C: uh , it might still be interesting to work on this
C: because the boundaries apparently that they would provide is just , um , starting of speech and end of speech uh , at the utterance level .
C: And C: Um .
G: With some some gap .
C: So G: I mean , with some pauses in the center ,
G: provided they meet that whatever the hang - over time which they are talking .
C: Yeah .
C: But when you have like , uh , five or six frames , both G: Yeah .
G: Then the they will just fill fill it up .
C: it it with G: I mean , th G: Yeah .
C: Yeah .
B: So if you could get at some of that , uh C: So B: although that 'd be hard .
C: Yeah .
C: It might be useful for , like , noise estimation , and a lot of other things that we want to work on .
B: But but G: Yeah .
B: Yeah .
B: Right .
B: OK .
C: But C: Mmm .
C: Yeah .
C: So I did I just started to test putting together two VAD which was was not much work actually .
C: Um ,
C: I im re - implemented a VAD that 's very close to the , um , energy - based VAD that , uh , the other Aurora guys use .
C: Um .
C: So , which is just putting a threshold on the noise energy ,
B: Mm - hmm .
C: and , detect detecting the first group of four frames that have a energy that 's above this threshold ,
C: and ,
C: uh ,
C: from this point , uh , tagging the frames there as speech .
C: So it removes the first silent portion portion of each utterance .
C: And it really removes it ,
C: um ,
C: still o on the noises where our MLP VAD doesn't work a lot .
B: Mmm .
C: Uh ,
C: and B: Cuz I would have thought that having some kind of spectral information ,
B: uh B: uh ,
B: you know , in the old days people would use energy
B: and zero crossings , for instance uh , would give you some better performance .
B: Right ?
B: Cuz you might have low - energy fricatives or or , uh stop consonants , or something like that .
C: Mm - hmm .
B: Uh .
C: Yeah .
C: So , your point is will be to u use whatever B: Oh , that if you d if you use purely energy and don't look at anything spectral , then you don't have a good way of distinguishing between low - energy speech components and nonspeech .
C: Mm - hmm .
B: And , um ,
B: just as a gross generalization , most nonsp many nonspeech noises have a low - pass kind of characteristic ,
B: some sort of slope .
B: And and most , um , low - energy speech components that are unvoiced have a a high - pass kind of characteristic C: Mm - hmm .
B: an upward slope .
C: Yeah .
B: So having some kind of a B: uh , you know , at the beginning of a of a of an S sound for instance , just starting in , it might be pretty low - energy ,
C: Mm - hmm .
B: but it will tend to have this high - frequency component .
B: Whereas , a a lot of rumble , and background noises , and so forth will be predominantly low - frequency .
B: Uh , you know , by itself it 's not enough to tell you ,
B: but it plus energy is sort of C: Yeah .
B: it plus energy plus timing information is sort of C: Mm - hmm .
B: I mean , if you look up in Rabiner and Schafer from like twenty - five years ago or something , that 's sort of what they were using then .
C: Mm - hmm .
B: So it 's it 's not a C: Mm - hmm .
F: Hmm .
C: So , yeah .
C: It it might be that what I did is C: so , removes like low , um , uh low - energy , uh , speech frames .
C: Because the way I do it is I just I just combine the two decisions C: so , the one from the MLP and the one from the energy - based with the with the and operator .
C: So ,
C: I only keep the frames where the two agree that it 's speech .
C: So if the energy - based dropped dropped low - energy speech , mmm , they they are they are lost .
C: Mmm .
B: Mm - hmm .
C: But s still , the way it 's done right now it it helps on on the noises where it seems to help on the noises where our VAD was not very good .
B: Well , I guess B: I mean ,
B: one could imagine combining them in different ways .
B: But but ,
B: I guess what you 're saying is that the the MLP - based one has the spectral information .
C: Yeah .
B: So .
C: But C: Yeah .
C: But the way it 's combined wi is maybe done C: Well , yeah .
B: Well , you can imagine C: The way I use a an a " AND " operator is C: So , it I , uh B: Is ?
C: The frames that are dropped by the energy - based system are are , uh , dropped , even if the , um , MLP decides to keep them .
B: Right .
B: Right .
B: And that might not be optimal ,
C: But , yeah .
B: but C: Mm - hmm .
B: but I mean , I guess in principle what you 'd want to do is have a  uh , a probability estimated by each one
A: No
B: and and put them together .
C: Yeah .
C: Mmm . M Yeah .
A: Something that that I 've used in the past is , um when just looking at the energy , is to look at the derivative .
A: And you make your decision when the derivative is increasing for so many frames .
A: Then you say that 's beginning of speech .
C: Uh - huh .
A: But , I 'm I 'm trying to remember if that requires that you keep some amount of speech in a buffer .
A: I guess it depends on how you do it .
A: But I mean , that 's that 's been a useful thing .
B: Yeah .
C: Mm - hmm .
F: Mm - hmm .
G: Yeah . Well , every everywhere has a delay associated with it .
G: I mean , you still have to k always keep a buffer ,
A: Mm - hmm .
G: then only make a decision because you still need to smooth the decision further .
A: Right .
A: Right .
G: So that 's always there .
A: Yeah .
A: OK .
C: Well , actually if I don't maybe don't want to work too much of on it right now .
C: I just wanted to to see if it 's  what I observed was the re was caused by this this VAD problem .
C: And it seems to be the case .
B: Mm - hmm .
C: Um .
C: Uh , the second thing is the this spectral subtraction .
C: Um .
C: Um ,
C: which I 've just started yesterday to launch a bunch of , uh , <nonvocalsound> twenty - five experiments ,
C: uh , with different , uh , values for the parameters that are used .
C: So ,
C: it 's the Makhoul - type spectral subtraction which use an over - estimation factor .
C: So , we substr I subtract more ,  um , <nonvocalsound> noise than the noise spectra that is estimated on the noise portion of the s uh , the utterances .
C: So I tried several , uh , over - estimation factors .
C: And after subtraction , I also add a constant noise ,
C: and I also try different , uh , noise , uh , values
C: and we 'll see what happen .
B: Hmm .
C: Mm - hmm .
B: OK .
C: Mm - hmm .
C: But st still when we look at the ,
C: um C: Well , it depends on the parameters that you use ,
C: but for moderate over - estimation factors and moderate noise level that you add , you st have a lot of musical noise .
C: Um .
C: On the other hand , when you subtract more and when you add more noise , you get rid of this musical noise
C: but maybe you distort a lot of speech .
C: So .
C: Well .
C: Mmm .
C: Well , it until now , it doesn't seem to help .
C: But
C: We 'll see .
C: So the next thing , maybe I what I will try to to do is just to try to smooth mmm , the , um to smooth the d the result of the subtraction ,
C: to get rid of the musical noise ,
C: using some kind of filter ,
C: or G: Can smooth the SNR estimate , also .
C: Yeah .
C: Right .
C: Mmm .
G: Your filter is a function of SNR . Hmm ?
C: Yeah .
C: So , to get something that 's would be closer to what you tried to do with Wiener filtering .
G: Yeah .
C: And C: Mm - hmm .
C: Yeah .
G: Actually , it 's ,
G: uh G: Uh .
G: I don't know ,
G: it 's G: go ahead .
G: And it 's C: It G: go ahead .
C: Maybe you can C: I think it 's C: That 's it for me .
G: OK .
G: So , uh G: u
G: th I 've been playing with this Wiener filter , like .
G: And there are there were some bugs in the program ,
G: so I was p initially trying to clear them up .
G: Because one of the bug was I was assuming that always the VAD uh , the initial frames were silence .
G: It always started in the silence state ,
G: but it wasn't for some utterances .
G: So the it wasn't estimating the noise initially ,
G: and then it never estimated ,
G: because I assumed that it was always silence .
C: Mm - hmm .
C: So this is on SpeechDat - Car Italian ?
G: Yeah .
G: SpeechDat - Car Italian .
C: So , in some cases s there are also G: Yeah .
G: There 're a few cases , actually , which I found later , that there are .
C: o