E: So again , it 's around fifty - six , fifty - seven .
E: Uh D: Cuz I notice the TI - digits number is exactly the same for these last two ?
E: Yeah , because I didn't E: For the France Telecom uh , spectral subtraction included in the our system , the TI - digits number are the right one ,
E: but not for the other system
E: because I didn't test it yet this system , including with spectral subtraction on the TI - digits data .
E: I just tested it on SpeechDat - Car .
G: Mm - hmm .
D: Ah !
D: So so that means the only thing G: So so so these numbers are simply E: This , we have to E: Yeah .
G: Yeah .
D: Yeah .
B: But this number .
G: OK .
E: Yes .
D: So you so you just should look at that fifty - eight perc point O nine percent and so on .
E: Right .
E: Right .
D: OK .
D: Good .
E: Mm - hmm .
E: Um ,
E: Yeah .
B: So this G: s
B: So by uh , by by reducing the noise a a decent threshold like minus thirty DB , it 's like Uh , you are like r r reducing the floor of the noisy regions , right ?
E: Yeah .
E: Yeah .
E: The floor is lower .
E: Um ,
B: Uh - huh .
E: mm - hmm .
D: I 'm sorry .
D: So when you say minus twenty - five or minus thirty DB , with respect to what ?
E: To the average um , speech energy
E: which is estimated on the world database .
D: OK ,
D: so basically you 're creating a signal - to - noise ratio of twenty - five or thirty DB ?
E: Yeah .
E: But it 's not G: I I I think what you do is this .
D: uh r
E: it it 's G: i When when you have this , after you subtracted it , I mean , then you get something w w with this , uh , where you set the values to zero
G: and then you simply add an additive constant again .
E: Yeah .
G: So you shift it somehow .
G: This this whole curve is shifted again .
E: Right .
D: But did you do that before the thresholding to zero ,
E: It 's D: or ?
E: But , it 's after the thresholding .
E: So ,
D: Oh ,
D: so you 'd really want to do it before ,
E: maybe D: right ?
E: maybe we might do it before ,
E: yeah .
D: Yeah ,
D: because then the then you would have less of that phenomenon .
E: Yeah .
G: E Hhh .
D: I think .
E: Uh E: Yeah .
D: c
E: But still , when you do this and you take the log after that , it it reduce the the variance .
D: Yeah ,
D: it it E: But D: Right .
E: Mmm ,
D: Yeah , that will reduce the variance .
D: That 'll help .
D: But maybe if you does do it before you get less of these funny - looking things he 's drawing .
G: Mm - hmm .
E: Um ,
B: So before it 's like adding this , col to the to the o exi original G: But but E: We would D: Right at the point where you 've done the subtraction .
B: OK .
D: Um , essentially you 're adding a constant into everything .
E: Mm - hmm .
G: But the way Stephane did it , it is exactly the way I have implemented in the phone ,
G: so .
D: Oh , yeah ,
D: better do it different , then .
D: Yeah .
E: Um .
E: Yeah .
D: Just you you just ta you just set it for a particular signal - to - noise ratio that you want ?
G: Yeah I I made s similar investigations like Stephane did here ,
G: just uh , adding this constant and and looking how dependent is it on the value of the constant
D: Yeah .
D: Yeah .
E: Mm - hmm .
G: and then , must choose them somehow to give on average the best results for a certain range of the signal - to - noise ratios .
E: Yeah .
D: Uh - huh .
E: Mm - hmm .
G: So E: Oh , it 's clear .
E: I should have gi given other results .
E: Also it 's clear when you don't add noise , it 's much worse .
E: Like , around five percent worse I guess .
D: Uh - huh .
E: And if you add too much noise it get worse also .
E: And it seems that right now this this is c a constant that does not depend on <comment> on anything that you can learn from the utterance .
E: It 's just a constant noise addition .
E: Um .
E: And I I think w w
D: I I 'm sorry .
E: I think D: Then then I 'm confused .
D: I thought D: you 're saying it doesn't depend on the utterance
D: but I thought you were adding an amount that was twenty - five DB down from the signal energy .
E: Yeah ,
E: so the way I did that , <comment> i I just measured the average speech energy of the all the Italian data .
D: Oh !
E: And then I I have I used this as mean speech energy .
E: Mm - hmm .
D: Oh , it 's just a constant amount over all .
E: Yeah .
E: And E: wha what I observed is that for Italian and Spanish , <comment> when you go to thirty and twenty - five DB , <comment> uh it it 's good .
B: OK .
B: Oh .
E: It stays In this range ,
E: it 's , uh , the p u
E: well , the performance of the this algorithm is quite good .
E: But for Finnish , you have a degradation already when you go from thirty - five to thirty
E: and then from thirty to twenty - five .
E: And I have the feeling that maybe it 's because just Finnish has a mean energy that 's lower than than the other databases .
E: And due to this the thresholds should be D: Yeah .
E: the the a the noise addition should be lower
D: But in I mean , in the real thing you 're not gonna be able to measure what people are doing over half an hour or an hour , or anything , right ?
E: and D: So you have to come up with this number from something else .
E: Yeah .
E: So G: Uh , but you are not doing it now language dependent ?
G: Or ?
E: It 's not .
E: It 's just something that 's fixed .
G: No .
G: It 's overall .
E: Yeah .
G: OK .
E: Mm - hmm .
E: Um E: Yeah , so I g
D: But what he is doing language dependent is measuring what that number i reference is that he comes down twenty - five down from .
E: No .
E: It No .
E: Because I did it I started working on Italian .
D: No ?
E: I obtained this average energy
D: Yeah .
E: and then I used this one .
B: For all the languages .
E: Yeah .
B: OK .
D: So it 's sort of arbitrary .
D: I mean , so if y if B: Yeah .
D: Yeah .
E: Yep .
E: Um ,
D: Yeah .
E: yeah , so the next thing is to use this as as maybe initialization
E: and then use something on - line .
D: Uh - huh .
D: Something more adaptive ,
E: But  And I expect improvement at least in Finnish because eh the way D: yeah .
D: OK .
E: Well ,
E: um ,
E: for Italian and Spanish it 's th this value works good but not necessarily for Finnish .
E: Mmm .
E: But unfortunately there is , like , this forty millisecond latency
E: and ,
E: um E: Yeah , so I would try to somewhat reduce this @ @ .
E: I already know that if I completely remove this latency , so . um , <comment> it um there is a three percent hit on Italian .
G: Mm - hmm .
B: d Does latency G: i
B: Sorry .
B: Go ahead .
G: Yeah .
G: Your your smoothing was @ @ <comment> uh , over this s so to say , the the factor of the Wiener .
G: And then it 's , uh G: What was it ?
G: This E: Mm - hmm .
G: this smoothing , it was over the subtraction factor , so to say .
E: It 's a smoothing over the the gain of the subtraction algorithm .
G: Was this done G: Mm - hmm .
G: And and you are looking into the future , into the past .
E: Right .
G: And smoothing .
G: Mm - hmm .
E: So , to smooth this thing .
E: Yeah .
E: Um G: And did did you try simply to smooth um to smooth the the t to to smooth stronger the the envelope ?
E: Um ,
E: no ,
E: I did not .
G: Mmm .
E: Mmm .
G: Because I mean , it should have a similar effect if you E: Yeah .
G: I mean , you you have now several stages of smoothing , so to say .
G: You start up .
G: As far as I remember you you smooth somehow the envelope ,
G: you smooth somehow the noise estimate ,
E: Mm - hmm .
G: and  and later on you smooth also this subtraction factor .
E: Mmm E: Uh , no ,
E: it 's it 's just the gain that 's smoothed actually
B: Uh , actually I d I do all the smoothing .
E: but it 's smoothed G: Ah .
G: Oh , it w it was you .
B: Yeah , yeah .
E: Uh Yeah .
G: Yeah .
E: Yeah .
G: Yeah .
E: No , in this case it 's just the gain .
G: Uh - huh .
E: And E: But the way it 's done is that um , for low gain , there is this non nonlinear smoothing actually .
E: For low gains um , I use the smoothed sm uh , smoothed version
G: Uh .
E: but for high gain @ @ <comment> it 's I don't smooth .
G: Mm - hmm .
G: I just , uh it Experience shows you , if if you do the G: The best is to do the smoo smoothing as early as possible .
E: Uh - huh .
G: So w when you start up .
G: I mean , you start up with the with the somehow with the noisy envelope .
E: Mm - hmm .
G: And , best is to smooth this somehow .