Professor D: Uh - huh .
PhD E: And if you add too much noise it get worse also . 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 . It 's just a constant noise addition . Um . And I I think w w
Professor D: I I 'm sorry . Then then I 'm confused .
PhD E: I think Professor D: I thought you 're saying it doesn't depend on the utterance but I thought you were adding an amount that was twenty - five DB down from the signal energy .
PhD E: Yeah , so the way I did that , {comment} i I just measured the average speech energy of the all the Italian data .
Professor D: Oh !
PhD E: And then I I have I used this as mean speech energy . Mm - hmm .
Professor D: Oh , it 's just a constant amount over all .
PhD E: Yeah . And PhD B: OK .
PhD 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 .
PhD B: Oh .
PhD E: It stays In this range , it 's , uh , the p u well , the performance of the this algorithm is quite good . But for Finnish , you have a degradation already when you go from thirty - five to thirty and then from thirty to twenty - five . And I have the feeling that maybe it 's because just Finnish has a mean energy that 's lower than than the other databases . And due to this the thresholds should be Professor D: Yeah .
PhD E: the the a the noise addition should be lower
Professor 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 ?
PhD E: and Professor D: So you have to come up with this number from something else .
PhD E: Yeah . So Professor G: Uh , but you are not doing it now language dependent ? Or ?
PhD E: It 's not . It 's just something that 's fixed .
Professor G: No . It 's overall .
PhD E: Yeah .
Professor G: OK .
PhD E: Mm - hmm . Um Professor D: But what he is doing language dependent is measuring what that number i reference is that he comes down twenty - five down from .
PhD E: Yeah , so I g No . It No .
Professor D: No ?
PhD E: Because I did it I started working on Italian . I obtained this average energy
Professor D: Yeah .
PhD E: and then I used this one .
PhD B: For all the languages . OK .
PhD E: Yeah .
Professor D: So it 's sort of arbitrary .
PhD B: Yeah .
Professor D: I mean , so if y if Yeah .
PhD E: Yep .
Professor D: Yeah .
PhD E: Um , yeah , so the next thing is to use this as as maybe initialization
Professor D: Uh - huh .
PhD E: and then use something on - line .
Professor D: Something more adaptive ,
PhD E: But  And I expect improvement at least in Finnish because eh the way Professor D: yeah . OK .
PhD E: Well , um , for Italian and Spanish it 's th this value works good but not necessarily for Finnish . Mmm . But unfortunately there is , like , this forty millisecond latency and , um Yeah , so I would try to somewhat reduce this @ @ . I already know that if I completely remove this latency , so . um , {comment} it um there is a three percent hit on Italian .
Professor G: Mm - hmm .
PhD B: d Does latency Professor G: i
PhD B: Sorry . Go ahead .
Professor G: Yeah . Your your smoothing was @ @ {comment} uh , over this s so to say , the the factor of the Wiener . And then it 's , uh What was it ? This PhD E: Mm - hmm .
Professor G: this smoothing , it was over the subtraction factor , so to say .
PhD E: It 's a smoothing over the the gain of the subtraction algorithm .
Professor G: Was this done Mm - hmm . And and you are looking into the future , into the past .
PhD E: Right .
Professor G: And smoothing .
PhD E: So , to smooth this thing .
Professor G: Mm - hmm .
PhD E: Yeah . Um Professor G: And did did you try simply to smooth um to smooth the the t to to smooth stronger the the envelope ?
PhD E: Um , no , I did not .
Professor G: Mmm .
PhD E: Mmm .
Professor G: Because I mean , it should have a similar effect if you PhD E: Yeah .
Professor G: I mean , you you have now several stages of smoothing , so to say . You start up . As far as I remember you you smooth somehow the envelope , you smooth somehow the noise estimate ,
PhD E: Mm - hmm . Mmm Professor G: and  and later on you smooth also this subtraction factor .
PhD E: Uh , no , it 's it 's just the gain that 's smoothed actually
PhD B: Uh , actually I d I do all the smoothing .
PhD E: but it 's smoothed Professor G: Ah . Oh , it w it was you .
PhD B: Yeah , yeah .
PhD E: Uh Yeah .
Professor G: Yeah .
PhD E: Yeah . No , in this case it 's just the gain .
Professor G: Yeah .
PhD E: And Professor G: Uh - huh .
PhD E: But the way it 's done is that um , for low gain , there is this non nonlinear smoothing actually . For low gains um , I use the smoothed sm uh , smoothed version but for high gain @ @ {comment} it 's I don't smooth .
Professor G: Uh . Mm - hmm . I just , uh it Experience shows you , if if you do the The best is to do the smoo smoothing as early as possible .
PhD E: Uh - huh .
Professor G: So w when you start up . I mean , you start up with the with the somehow with the noisy envelope .
PhD E: Mm - hmm .
Professor G: And , best is to smooth this somehow .
PhD E: Mm - hmm . Uh , yeah , I could try this . Um .
Professor G: And PhD B: So , before estimating the SNR , @ @ smooth the envelope .
Professor G: Yeah . Yeah . Uh - huh .
PhD E: Mm - hmm . But Yeah . Then I I would need to find a way to like smooth less also when there is high energy . Cuz I noticed that it it helps a little bit to s like smooth more during low energy portions and less during speech ,
Professor G: Yes , y
PhD E: because if you smooth then y you kind of distort the speech .
Professor G: Yeah . Yeah .
PhD E: Um .
Professor G: Right .
PhD E: Mm - hmm .
Professor G: Yeah , I think when w you you could do it in this way that you say , if you if I 'm you have somehow a noise estimate ,
PhD E: Mm - hmm .
Professor G: and , if you say I 'm I 'm with my envelope I 'm close to this noise estimate ,
PhD E: Yeah .
Professor G: then you have a bad signal - to - noise ratio and then you you would like to have a stronger smoothing .
PhD E: Mm - hmm .
Professor G: So you could you could base it on your estimation of the signal - to - noise ratio on your actual PhD E: Mm - hmm . Mm - hmm . Mmm .
PhD B: Yeah , or some silence probability from the VAD if you have PhD E: Um , yeah , but I don't trust the current VAD . So .
PhD B: Yeah , uh , so not not right now maybe .
PhD E: Well , maybe .
Professor D: The VAD later will be much better .
PhD E: Maybe .
Professor D: Yeah . So . I see .
PhD F: So is that it ?
PhD E: Uh , fff {comment} I think that 's it . Yeah . Uh .
Professor G: s So to summarize the performance of these , SpeechDat - Car results is similar than than yours so to say .
PhD B: Yeah , so the fifty - eight is like the be some fifty - six point PhD E: Yeah .
Professor G: Y you have you have fifty - six point four
PhD B: Yeah , that 's true .
Professor G: and and  and dependent on this additive constant , it is s better or or worse .
PhD E: Yeah .
PhD B: Slightly better .
PhD E: Mm - hmm .
PhD H :
PhD B: Yeah .
PhD E: Mm - hmm .
Professor G: Yeah .
PhD E: And , yeah , 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 ,
PhD B: Yeah . Yeah , you you caught up .
PhD E: because PhD B: Yep , that 's true .
PhD 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 or PhD B: Yeah .
PhD E: It 's better for high mismatch , right ?
PhD B: Yeah , it 's better for high mismatch .
PhD E: Mm - hmm . But a little bit worse for well matched .
PhD B: So over all it gets , yeah , worse for the well matched condition , so y
PhD E: Uh - huh .
PhD F: So we need to combine these two .
PhD B: Uh , that 's that 's the best thing , is like the French Telecom system is optimized for the well matched condition . They c
PhD E: Mm - hmm .
PhD B: Yeah . So they know that the weighting is good for the well matched , and so there 's everywhere the well matched 's s s performance is very good for the French Telecom .
PhD E: Yeah .
Professor G: Mm - hmm .
PhD E: Mm - hmm .
PhD B: T we are we may also have to do something similar @ @ .
PhD E: Mm - hmm .
Professor D: Well , our tradition here has always been to focus on the mismatched .
PhD B: Um the Professor D: Cuz it 's more interesting .
Professor G: Mu - my mine was it too , I mean .
Professor D: Yeah .
Professor G: Before I started working on this Aurora .
Professor D: Yeah .
Professor G: so .
Professor D: Yeah . Yeah . OK .
PhD F: Carmen ? Do you , uh PhD H: Well , I only say that the this is , a summary of the of all the VTS experiments and say that the result in the last {comment} um , for Italian the last experiment for Italian , are bad . I make a mistake when I write . Up at D I copy one of the bad result .
PhD B: So you PhD H: And There . You know , this . Um , well . If we put everything , we improve a lot u the spectral use of the VTS but the final result are not still mmm , good like the Wiener filter for example . I don't know . Maybe it 's @ @ {comment} it 's possible to to have the same result .
PhD B: That 's somewhere PhD H: I don't know exactly . Mmm . Because I have , mmm , {comment} worse result in medium mismatch and high mismatch .
PhD B: You s you have a better r Yeah . You have some results that are good for the high mismatch .
PhD H: And Yeah . I someti are more or less similar but but are worse . And still I don't have the result for TI - digits . The program is training . Maybe for this weekend I will have result TI - digits and I can complete that s like this . Well .
Professor D: Uh . Right .
PhD H: One thing that I {comment} note are not here in this result but are speak are spoken before with Sunil I I improve my result using clean LDA filter .
PhD B: Mm - hmm .
Professor D: Mm - hmm .
PhD H: If I use , eh , the LDA filter that are training with the noisy speech , that hurts the res my results .
Professor D: So what are these numbers here ? Are these with the clean or with the noisy ?
PhD H: This is with the clean .
Professor D: OK .
PhD H: With the noise I have worse result , that if I doesn't use it .
Professor D: Uh - huh .
PhD H: But m that may be because with this technique we are using really really clean speech . The speech the {comment} representation that go to the HTK is really clean speech because it 's from the dictionary , the code book and maybe from that . I don't know .
PhD E: Mm - hmm .
PhD H: Because I think that you did some experiments using the two the two LDA filter , clean and noi and noise ,
PhD E: It 's PhD H: and it doesn't matter too much .
PhD E: Um , yeah , I did that but it doesn't matter on SpeechDat - Car , but , it matters , uh , a lot on TI - digits .
PhD B: Using the clean filter .
PhD H: It 's better to use clean .
PhD E: Yeah , d uh , it 's much better when you we used the clean derived LDA filter .
PhD H: Mm - hmm . Maybe you can do d also this .
PhD B: Yeah .
PhD H: To use clean speech .
PhD B: Yeah , I 'll try .
PhD E: Uh , but , yeah , Sunil in in your result it 's PhD B: I I 'll try the cle No , I I my result is with the noisy noisy LDA .
PhD E: It 's with the noisy one . Yeah .
PhD B: Yeah .
Professor D: Oh !
PhD B: It 's with the noisy . Yeah . It 's it 's not the clean LDA .
PhD E: So Professor D: Um PhD B: It 's In in the front sheet , I have like like the summary . Yeah .
Professor D: And and your result {comment} is with the PhD E: It 's with the clean LDA .
PhD B: Oh . This is Your results are all with the clean LDA result ?
PhD H: Yeah , with the clean LDA .
PhD B: OK . @ @ .
PhD E: Yeah .
PhD B :
PhD E: And in your case it 's all all noisy ,
PhD H: Is that the reason ?
PhD B: All noisy , yeah .
PhD E: yeah . But PhD H: And PhD B: Uh PhD E: Yeah .
PhD B: Uh PhD E: But I observe my case it 's in , uh , uh , at least on SpeechDat - Car it doesn't matter but TI - digits it 's like two or three percent absolute , uh , {comment} better .
PhD B: On TI - digits this matters . Absolute . Uh Professor D: So you really might wanna try the clean I think .
PhD E: So if PhD B: Yeah , I I I will have to look at it . Yeah , that 's true .
Professor D: Yeah . Yeah , that could be sizeable right there .
PhD H: And this is everything .
Professor G: Yeah .
Professor D: OK .
Professor G: Maybe you you are leaving in in about two weeks Carmen . No ?
PhD H: Yeah .
Professor G: Yeah . So I mean , if if if I would put it put on the head of a project mana manager I I I I would say , uh , um I mean there is not so much time left now .
Professor D: Be my guest .
Professor G: I mean , if  um , what what I would do is I I I would pick @ @ {comment} the best consolation , which you think , and c create create all the results for the whole database that you get to the final number as as Sunil did it
PhD H: And prepare at the s
Professor G: and um and maybe also to to write somehow a document where you describe your approach , and what you have done .