E: Mm - hmm .
E: Uh , yeah ,
E: I could try this .
E: Um .
G: And B: So , before estimating the SNR , @ @ smooth the envelope .
G: Yeah .
G: Yeah .
G: Uh - huh .
E: Mm - hmm .
E: But E: Yeah .
E: Then I I would need to find a way to like smooth less also when there is high energy .
E: Cuz I noticed that it it helps a little bit to s like smooth more during low energy portions and less during speech ,
G: Yes ,
G: y
E: because if you smooth then y you kind of distort the speech .
G: Yeah .
G: Yeah .
G: Right .
E: Um .
E: Mm - hmm .
G: Yeah , I think when
G: w you you could do it in this way that you say , if you if I 'm G: you have somehow a noise estimate ,
E: Mm - hmm .
G: and , if you say I 'm I 'm with my envelope
G: I 'm close to this noise estimate ,
E: Yeah .
G: then you have a bad signal - to - noise ratio and then you you would like to have a stronger smoothing .
E: Mm - hmm .
G: So you could you could base it on your estimation of the signal - to - noise ratio on your actual E: Mm - hmm .
E: Mm - hmm .
E: Mmm .
B: Yeah ,
B: or some silence probability from the VAD if you have E: Um ,
E: yeah ,
E: but I don't trust the current VAD .
E: So .
B: Yeah ,
B: uh , so not not right now maybe .
E: Well , maybe .
E: Maybe .
D: The VAD later will be much better .
D: Yeah .
D: So .
D: I see .
F: So is that it ?
E: Uh , fff <comment> I think that 's it .
E: Yeah .
E: Uh .
G: s So to summarize the performance of these , SpeechDat - Car results is similar than than yours so to say .
B: Yeah ,
E: Yeah .
B: so the fifty - eight is like the be some fifty - six point G: Y you have you have fifty - six point four
G: and and  and dependent on this additive constant , it is s better or or worse .
B: Yeah ,
B: that 's true .
E: Yeah .
B: Slightly better .
E: Mm - hmm .
B: Yeah .
G: Yeah .
E: Mm - hmm .
E: And , yeah ,
E: i i i the condition where it 's better than your approach , it 's it just because maybe it 's better on well matched and that the weight on well matched is is bigger ,
B: Yeah .
B: Yeah , you you caught up .
E: because B: Yep ,
B: that 's true .
E: if you don't weigh differently the different condition , you can see that your well , the win the two - stage Wiener filtering is maybe better
E: or E: It 's better for high mismatch , right ?
B: Yeah .
B: Yeah ,
B: it 's better for high mismatch .
E: Mm - hmm .
E: But a little bit worse for well matched .
B: So over all it gets , yeah , worse for the well matched condition ,
B: so y
E: Uh - huh .
F: So we need to combine these two .
B: Uh ,
B: that 's that 's the best thing , is like the French Telecom system is optimized for the well matched condition .
E: Mm - hmm .
B: They c
B: Yeah .
B: So they know that the weighting is good for the well matched ,
B: and so there 's B: everywhere the well matched 's s s performance is very good for the French Telecom .
E: Yeah .
G: Mm - hmm .
E: Mm - hmm .
B: T we are we may also have to do something similar @ @ .
E: Mm - hmm .
D: Well , our tradition here has always been to focus on the mismatched .
B: Um the D: Cuz it 's more interesting .
G: Mu - my mine was it too , I mean .
D: Yeah .
G: Before I started working on this Aurora .
G: so .
D: Yeah .
D: Yeah .
D: Yeah .
D: OK .
F: Carmen ?
F: Do you , uh H: Well , I only say that the this is , a summary of the of all the VTS experiments
H: and say that the result in the last <comment> um , for Italian the last experiment for Italian , are bad .
H: I make a mistake when I write .
H: Up at D I copy one of the bad result .
B: So you H: And H: There . You know , this .
H: Um ,
H: well .
H: If we put everything , we improve a lot u the spectral use of the VTS
H: but the final result are not still mmm , good like the Wiener filter for example .
H: I don't know .
H: Maybe it 's @ @ <comment> it 's possible to to have the same result .
B: That 's somewhere