B: Well , depending on its size B: Well , one question is , is it on the , um , server side or is it on the terminal side ?
B: Uh , if it 's on the server side , it you probably don't have to worry too much about size .
A: Mm - hmm .
B: So that 's kind of an argument for that .
B: We do still , however , have to consider its latency .
B: So the issue is is , um , for instance , could we have a neural net that only looked at the past ?
A: Right .
B: Um , what we 've done in uh in the past is to use the neural net , uh , to transform , um , all of the features that we use .
B: So this is done early on .
B: This is essentially , um , um I guess it 's it 's more or less like a spee a speech enhancement technique here B: right ? A: Mm - hmm .
B: where we 're just kind of creating new if not new speech at least new new FFT 's
B: that that have you know , which could be turned into speech E: Mm - hmm .
B: uh , that that have some of the noise removed .
A: Mm - hmm .
B: Um , after that we still do a mess of other things to to produce a bunch of features .
A: Right .
B: And then those features are not now currently transformed by the neural net .
B: And then the the way that we had it in our proposal - two before , we had the neural net transformed features and we had the untransformed features ,
B: which I guess you you actually did linearly transform with the KLT ,
E: Yeah . Yeah . Right .
B: but but but uh , to orthogonalize them B: but  but they were not , uh , processed through a neural net .
B: And Stephane 's idea with that , as I recall , was that you 'd have one part of the feature vector that was very discriminant and another part that wasn't ,
A: Mm - hmm .
B: uh , which would smooth things a bit for those occasions when , uh , the testing set was quite different than what you 'd trained your discriminant features for .
B: So , um , all of that is is , uh still seems like a good idea .
B: The thing is now we know some other constraints .
B: We can't have unlimited amounts of latency .
B: Uh , y you know , that 's still being debated by the by people in Europe
B: but , uh , no matter how they end up there , it 's not going to be unlimited amounts ,
B: so we have to be a little conscious of that .
A: Yeah .
B: Um .
B: So there 's the neural net issue .
B: There 's the VAD issue .
B: And , uh , there 's the second stream thing .
B: And I think those that we last time we agreed that those are the three things that have to get , uh , focused on .
A: What was the issue with the VAD ?
B: Well , better <comment> ones are good .
A: And so the w the default , uh , boundaries that they provide are they 're OK , but they 're not all that great ?
B: I guess they still allow two hundred milliseconds on either side or some ?
E: Mm - hmm .
B: Is that what the deal is ?
E: Uh , so th um , they keep two hundred milliseconds at the beginning and end of speech . And they keep all the A: Outside the beginnings and end .
E: Yeah .
E: And all the speech pauses ,
A: Uh - huh .
E: which is Sometimes on the SpeechDat - Car you have pauses that are more than one or two seconds .
A: Wow .
E: More than one second for sure .
E: Um .
A: Hmm .
E: Yeah .
E: And , yeah , it seems to us that this way of just dropping the beginning and end is not E: We cou we can do better , I think ,
A: Mm - hmm .
E: because , um , with this way of dropping the frames they improve over the baseline by fourteen percent
E: and Sunil already showed that with our current VAD we can improve by more than twenty percent .
A: On top of the VAD that they provide ?
C: No .
E: Just using either their VAD or our current VAD .
C: Our way .
A: Oh , OK .
E: So , our current VAD is is more than twenty percent ,
E: while their is fourteen .
A: Theirs is fourteen ?
E: Yeah .
A: I see .
A: Huh .
E: So .
E: Yeah .
E: And another thing that we did also is that we have all this training data for let 's say , for SpeechDat - Car .
E: We have channel zero which is clean ,
E: channel one which is far - field microphone .
E: And
E: if we just take only the , um , VAD probabilities computed on the clean signal and apply them on the far - field , uh , test utterances , then results are much better .
A: Mm - hmm .
E: In some cases it divides the error rate by two .
A: Wow .
E: So it means that there are stim <comment> still E: If if we can have a good VAD , well , it would be great .
A: How how much latency does the , uh does our VAD add ?
A: Is it significant ,
E: Uh , right now it 's , um , a neural net with nine frames .
A: or ?
E: So it 's forty milliseconds plus , um , the rank ordering ,
E: which , uh , should be
C: Like another ten frames .
E: ten E: Yeah .
E: So , right now it 's one hundred and forty milliseconds .
D: Rank .
D: Oh .
B: With the rank ordering ?
B: I 'm sorry .
C: The the the smoothing the m the the filtering of the probabilities .
E: The The , um C: on the R .
E: Yeah . It 's not a median filtering .
E: It 's just We don't take the median value . We take something E: Um , so we have eleven , um , frames .
B: Oh , this is for the VAD .
E: And C: Yeah .
E: for the VAD , yeah E: and we take th the third .
C: Yeah .
B: Oh , OK .
C: Yeah .
D: Dar
E: Um .
B: Yeah .
B: Um .
E: Mmm .
B: So <comment> Yeah , I was just noticing on this that it makes reference to delay .
B: So what 's the ? If you ignore B: Um , the VAD is sort of in in parallel ,
B: isn't i isn't it ,
B: with with the ? I mean , it isn't additive with the the , uh , LDA and the Wiener filtering , and so forth .
C: The LDA ?
C: Yeah . So so what happened right now , we removed the delay of the LDA .
B: Right ?
E: Mm - hmm .
B: Yeah .
C: So we I mean , if so if we if so which is like if we reduce the delay of VA
C: So , the f the final delay 's now ba is f determined by the delay of the VAD ,
C: because the LDA doesn't have any delay .
C: So if we re if we reduce the delay of the VAD , I mean , it 's like effectively reducing the delay .
A: How how much , uh , delay was there on the LDA ?
C: So the LDA and the VAD both had a hundred millisecond delay .
C: So and they were in parallel ,
C: so which means you pick either one of them A: Mmm .
C: the the biggest , whatever .
B: Mm - hmm .
A: I see .
C: So , right now the LDA delays are more .
B: And there A: Oh , OK .
B: And there didn't seem to be any , uh , penalty for that ?
C: Pardon ?
B: There didn't seem to be any penalty for making it causal ?
C: Oh , no . It actually made it , like , point one percent better or something , actually .
B: OK .
C: Or something like that
B: Well , may as well , then .
C: and B: And he says Wiener filter is is forty milliseconds delay .
C: Yeah . So that 's the one which Stephane was discussing , like E: Mmm .
B: So is it ?
B: The smoothing ?
C: Yeah . The you smooth it and then delay the decision by C: So .
B: Right .
B: OK .
B: So that 's that 's really not not bad .
B: So we may in fact we 'll see what they decide . We may in fact have , um , the the , uh , latency time available for to have a neural net .
B: I mean , sounds like we probably will .
C: Mm - hmm .
B: So .
B: That 'd be good .
B: Cuz I cuz it certainly always helped us before .
B: So .
B: Uh .
A: What amount of latency are you thinking about when you say that ?
B: Well , they 're you know , they 're disputing it .
B: You know , they 're saying , uh one group is saying a hundred and thirty milliseconds and another group is saying two hundred and fifty milliseconds .
A: Mmm .
B: Two hundred and fifty is what it was before actually .
B: So ,
A: Oh .
B: uh , some people are lobbying lobbying <comment> to make it shorter .
A: Hmm .
B: Um .
B: And , um .
A: Were you thinking of the two - fifty or the one - thirty when you said we should have enough for the neural net ?
B: Well , it just it when we find that out it might change exactly how we do it , is all .
B: I mean , how much effort do we put into making it causal ?
A: Oh , OK .
B: I mean , I think the neural net will probably do better if it looks at a little bit of the future .
A: Mm - hmm .
B: But , um , it will probably work to some extent to look only at the past .
B: And we ha you know , limited machine and human time , and effort .
B: And , you know , how how much time should we put into into that ?
B: So it 'd be helpful if we find out from the the standards folks whether , you know , they 're gonna restrict that or not .