Meeting transcript:
speaker: A
text: OK ,
speaker: C
text: Eight , eight ?
speaker: A
text: we 're going .
speaker: D
text: This is three .
speaker: D
text: Yep .
speaker: C
text: Three .
speaker: D
text: Yep .
speaker: B
text: Test .
speaker: B
text: Hmm .
speaker: B
text: Let 's see .
speaker: B
text: Move it bit .
speaker: B
text: Test ?
speaker: B
text: Test ?
speaker: B
text: OK ,
speaker: B
text: I guess it 's alright .
speaker: B
text: So ,
speaker: B
text: let 's see .
speaker: B
text: Yeah , Barry 's not here and Dave 's not here .
speaker: B
text: Um , I can say about <disfmarker> just q just quickly to get through it , that Dave and I submitted this ASRU .
speaker: A
text: This is for
speaker: A
text: ASRU .
speaker: B
text: Yeah .
speaker: B
text: So .
speaker: B
text: Um .
speaker: B
text: Yeah , it 's <disfmarker> it 's interesting .
speaker: B
text: I mean , basically we 're dealing with rever reverberation ,
speaker: B
text: and , um , when we deal with pure reverberation , the technique he 's using works really , really well .
speaker: B
text: Uh , and when they had the reverberation here , uh , we 'll measure the signal - to - noise ratio
speaker: B
text: and it 's , uh , about nine DB .
speaker: D
text: Hmm .
speaker: B
text: So ,
speaker: B
text: um ,
speaker: B
text: a fair amount of <disfmarker>
speaker: A
text: You mean , from the actual , uh , recordings ?
speaker: D
text: k
speaker: B
text: Yeah .
speaker: A
text: It 's nine DB ?
speaker: B
text: Yeah .
speaker: B
text: Um <disfmarker>
speaker: B
text: And actually it brought up a question which may be relevant to the Aurora stuff too .
speaker: B
text: Um , I know that when you figured out the filters that we 're using for the Mel scale , there was some experimentation that went on at <disfmarker> at , uh <disfmarker> at OGI .
speaker: B
text: Um ,
speaker: B
text: but one of the differences that we found between the two systems that we were using , <comment> the <disfmarker> the Aurora HTK system baseline system <comment> and the system that we were <disfmarker> the <disfmarker> the uh , other system we were using , the uh , the SRI system , was that the SRI system had maybe a , um , hundred hertz high - pass .
speaker: D
text: Yep .
speaker: B
text: And the , uh , Aurora HTK , it was like twenty .
speaker: D
text: S sixty - four .
speaker: D
text: S sixty - four .
speaker: B
text: Uh .
speaker: B
text: Sixty - four ?
speaker: D
text: Yeah ,
speaker: B
text: Uh .
speaker: D
text: if you 're using the baseline .
speaker: B
text: Is that the ba band center ?
speaker: D
text: No ,
speaker: D
text: the edge .
speaker: B
text: The edge is really , uh , sixty - four ?
speaker: D
text: Yeah .
speaker: B
text: For some reason , uh , Dave thought it was twenty ,
speaker: D
text: So the , uh , center would be somewhere around like hundred
speaker: B
text: but .
speaker: D
text: and <disfmarker> hundred and <disfmarker> hundred <disfmarker> hundred and <disfmarker> maybe <disfmarker> it 's like <disfmarker> fi hundred hertz .
speaker: B
text: But do you know , for instance , h how far down it would be at twenty hertz ?
speaker: B
text: What the <disfmarker> how much rejection would there be at twenty hertz , let 's say ?
speaker: D
text: At twenty hertz .
speaker: B
text: Yeah ,
speaker: B
text: any idea what the curve looks like ?
speaker: D
text: Twenty hertz frequency <disfmarker>
speaker: D
text: Oh , it 's <disfmarker> it 's zero at twenty hertz , right ?
speaker: D
text: The filter ?
speaker: C
text: Yea - actually , the left edge of the first filter is at sixty - four .
speaker: D
text: Sixt - s sixty - four .
speaker: C
text: So <disfmarker>
speaker: D
text: So anything less than sixty - four is zero .
speaker: C
text: Mmm .
speaker: B
text: It 's actually set to zero ?
speaker: B
text: What kind of filter is that ?
speaker: D
text: Yeah .
speaker: C
text: Yeah .
speaker: B
text: Is this <disfmarker> oh , from the <disfmarker> from <disfmarker>
speaker: C
text: It <disfmarker>
speaker: C
text: This is the filter bank in the frequency domain that starts at sixty - four .
speaker: B
text: Oh , so you , uh <disfmarker> so you really set it to zero , the FFT ?
speaker: D
text: Yeah ,
speaker: D
text: yeah .
speaker: C
text: Yeah .
speaker: D
text: So it 's <disfmarker> it 's a weight on the ball spectrum .
speaker: D
text: Triangular weighting .
speaker: B
text: Right .
speaker: B
text: OK .
speaker: B
text: Um <disfmarker>
speaker: B
text: OK .
speaker: B
text: So that 's <disfmarker> that 's a little different than Dave thought , I think .
speaker: B
text: But <disfmarker> but ,
speaker: B
text: um ,
speaker: B
text: still , it 's possible that we 're getting in some more noise .
speaker: B
text: So I wonder , is it <disfmarker> @ @ Was there <disfmarker> their experimentation with , uh , say , throwing away that filter or something ?
speaker: B
text: And , uh <disfmarker>
speaker: D
text: Uh , throwing away the first ?
speaker: B
text: Yeah .
speaker: D
text: Um ,
speaker: D
text: yeah , we <disfmarker> we 've tried including the full <disfmarker> full bank .
speaker: D
text: Right ?
speaker: D
text: From zero to four K .
speaker: C
text: Mm - hmm .
speaker: D
text: And that 's always worse than using sixty - four hertz .
speaker: B
text: Right ,
speaker: B
text: but the question is , whether sixty - four hertz is <disfmarker> is , uh , too , uh , low .
speaker: D
text: Yeah ,
speaker: D
text: I mean , make it a hundred or so ?
speaker: B
text: Yeah .
speaker: D
text: I t I think I 've tried a hundred and it was more or less the same , or slightly worse .
speaker: B
text: On what test set ?
speaker: D
text: On the same , uh , SpeechDat - Car ,
speaker: D
text: Aurora .
speaker: B
text: Um , it was on the SpeechDat - Car .
speaker: D
text: Yeah .
speaker: D
text: So I tried a hundred to four K .
speaker: D
text: Yeah .
speaker: B
text: Um ,
speaker: D
text: So it was <disfmarker>
speaker: B
text: and on <disfmarker> and on the , um , um , <vocalsound> TI - digits also ?
speaker: D
text: No , no , no .
speaker: D
text: I think I just tried it on SpeechDat - Car .
speaker: B
text: Mmm .
speaker: B
text: That 'd be something to look at sometime
speaker: B
text: because what , um , eh , he was looking at was performance in this room .
speaker: D
text: Mm - hmm .
speaker: B
text: Would that be more like <disfmarker>
speaker: B
text: Well , you 'd think that 'd be more like SpeechDat - Car ,
speaker: B
text: I guess ,
speaker: B
text: in terms of the noise .
speaker: B
text: The SpeechDat - Car is more , uh , sort of roughly stationary , a lot of it .
speaker: D
text: Yeah .
speaker: B
text: And <disfmarker> and TI - digits maybe is not so much as <disfmarker>
speaker: C
text: Mm - hmm .
speaker: D
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: Mm - hmm .
speaker: B
text: OK .
speaker: B
text: Well , maybe it 's not a big deal .
speaker: B
text: But , um <disfmarker>
speaker: B
text: Anyway , that was just something we wondered about .
speaker: B
text: But , um ,
speaker: B
text: uh , certainly a lot of the noise , uh , is , uh , below a hundred hertz .
speaker: B
text: Uh , the signal - to - noise ratio , you know , looks a fair amount better if you <disfmarker> if you high - pass filter it from this room .
speaker: D
text: Yeah .
speaker: B
text: But , um <disfmarker>
speaker: B
text: but it 's still pretty noisy .
speaker: B
text: Even <disfmarker> even for a hundred hertz up , it 's <disfmarker> it 's still fairly noisy .
speaker: C
text: Mm - hmm .
speaker: B
text: The signal - to - noise ratio is <disfmarker> is <disfmarker> is actually still pretty bad .
speaker: A
text: Hmm .
speaker: B
text: So , um ,
speaker: B
text: I mean , the main <disfmarker> the <disfmarker> the <disfmarker>
speaker: A
text: So that 's on th that 's on the f the far field ones though , right ?
speaker: B
text: Yeah ,
speaker: B
text: that 's on the far field .
speaker: A
text: Yeah .
speaker: B
text: Yeah , the near field 's pretty good .
speaker: A
text: So wha what is , uh <disfmarker> what 's causing that ?
speaker: B
text: Well , we got a <disfmarker> a video projector in here ,
speaker: B
text: uh ,
speaker: B
text: and , uh <disfmarker> which we keep on during every <disfmarker> every session we record ,
speaker: A
text: Yeah .
speaker: B
text: which , you know , I <disfmarker> I <disfmarker> w we were aware of
speaker: B
text: but <disfmarker> but we thought it wasn't a bad thing .
speaker: A
text: Uh - huh .
speaker: B
text: I mean , that 's a nice noise source .
speaker: A
text: Yeah .
speaker: B
text: Uh , and there 's also the , uh <disfmarker> uh , air conditioning .
speaker: A
text: Hmm .
speaker: B
text: Which , uh , you know , is a pretty low frequency kind of thing .
speaker: A
text: Mm - hmm .
speaker: B
text: But <disfmarker> but , uh <disfmarker>
speaker: B
text: So , those are <disfmarker> those are major components , I think ,
speaker: A
text: I see .
speaker: B
text: uh , for the stationary kind of stuff .
speaker: A
text: Mmm .
speaker: B
text: Um ,
speaker: B
text: but , um ,
speaker: B
text: it , uh <disfmarker>
speaker: B
text: I guess , I <disfmarker> maybe I said this last week too
speaker: B
text: but it <disfmarker> it <disfmarker> it really became apparent to us that we need to <disfmarker> to take account of noise .
speaker: B
text: And , uh ,
speaker: B
text: so I think when <disfmarker> when he gets done with his prelim study I think <vocalsound> one of the next things we 'd want to do is to take this , uh <disfmarker> uh , noise , uh , processing stuff and <disfmarker> and , uh <disfmarker> uh , synthesize some speech from it .
speaker: B
text: And then <disfmarker>
speaker: A
text: When are his prelims ?
speaker: B
text: Um , I think in about , um , a little less than two weeks .
speaker: A
text: Oh .
speaker: A
text: Wow .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: So .
speaker: B
text: Uh , it might even be sooner .
speaker: B
text: Uh , let 's see , this is the sixteenth ,
speaker: B
text: seventeenth ?
speaker: B
text: Yeah , I don't know if he 's before <disfmarker>
speaker: B
text: It might even be in a week .
speaker: A
text: So , I
speaker: B
text: A week ,
speaker: A
text: Huh .
speaker: B
text: week and a half .
speaker: A
text: I <disfmarker> I guessed that they were gonna do it some time during the semester
speaker: A
text: but they 'll do it any time , huh ?
speaker: B
text: They seem to be <disfmarker>
speaker: B
text: Well , the semester actually is starting up .
speaker: A
text: Is it already ?
speaker: B
text: Yeah ,
speaker: B
text: the semester 's late <disfmarker> late August they start here .
speaker: A
text: Yikes .
speaker: B
text: So they do it right at the beginning of the semester .
speaker: A
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: So , uh <disfmarker>
speaker: B
text: Yep .
speaker: B
text: I mean , that <disfmarker> that was sort of one <disfmarker>
speaker: B
text: I mean ,
speaker: B
text: the overall results seemed to be first place in <disfmarker> in <disfmarker> in the case of either , um , artificial reverberation or a modest sized training set .
speaker: B
text: Uh , either way , uh , i uh , it helped a lot .
speaker: B
text: And <disfmarker> But if you had a <disfmarker> a really big training set , a recognizer , uh , system that was capable of taking advantage of a really large training set <disfmarker>
speaker: B
text: I thought that <disfmarker> One thing with the HTK is that is has the <disfmarker> as we 're using <disfmarker> the configuration we 're using is w s is <disfmarker> being bound by the terms of Aurora ,
speaker: B
text: we have all those parameters just set as they are .
speaker: B
text: So even if we had a hundred times as much data , we wouldn't go out to , you know , ten or t or a hundred times as many Gaussians or anything .
speaker: B
text: So ,
speaker: B
text: um , it 's kind of hard to take advantage of <disfmarker> of <disfmarker> of big chunks of data .
speaker: C
text: Mm - hmm .
speaker: D
text: Mmm , yeah .
speaker: B
text: Uh , whereas the other one does sort of expand as you have more training data .
speaker: B
text: It does it automatically , actually .
speaker: B
text: And so ,
speaker: B
text: um ,
speaker: B
text: uh ,
speaker: B
text: that one really benefited from the larger set .
speaker: B
text: And it was also a diverse set with different noises and so forth .
speaker: B
text: Uh , so , um ,
speaker: B
text: that , uh <disfmarker> that seemed to be <disfmarker>
speaker: B
text: So , if you have that <disfmarker> that better recognizer that can <disfmarker> that can build up more parameters , and if you , um , have the natural room , which in this case has a p a pretty bad signal - to - noise ratio , then in that case , um , the right thing to do is just do <disfmarker> u use speaker adaptation . And <disfmarker> and not bother with <disfmarker> with this acoustic , uh , processing .
speaker: B
text: But I think that that would not be true if we did some explicit noise - processing as well as , uh , the convolutional kind of things we were doing .
speaker: C
text: Mm - hmm .
speaker: B
text: So .
speaker: B
text: That 's sort of what we found .
speaker: D
text: Hmm .
speaker: A
text: I , um <disfmarker> <vocalsound> uh , started working on the uh <disfmarker> Mississippi State recognizer .
speaker: D
text: Oh ,
speaker: A
text: So , I got in touch with Joe and <disfmarker> and , uh , from your email and things like that .
speaker: D
text: OK .
speaker: A
text: And , uh , they added me to the list <disfmarker>
speaker: A
text: uh , the mailing list .
speaker: D
text: OK ,
speaker: D
text: great .
speaker: A
text: And he gave me all of the pointers and everything that I needed .
speaker: A
text: And so I downloaded the , um <disfmarker>
speaker: A
text: There were two things , uh , that they had to download .
speaker: A
text: One was the , uh , I guess the software .
speaker: A
text: And another wad <disfmarker> was a , um , sort of like a sample <disfmarker> a sample run .
speaker: A
text: So I downloaded the software and compiled all of that .
speaker: A
text: And it compiled fine .
speaker: D
text: Eight .
speaker: D
text: Oh , eh , great .
speaker: A
text: No problems .
speaker: A
text: And , um , I grabbed the sample stuff
speaker: A
text: but I haven't , uh , compiled it .
speaker: D
text: That sample was released only yesterday or the day before , right ?
speaker: A
text: No <disfmarker>
speaker: A
text: Well , I haven't grabbed that one yet .
speaker: A
text: So there 's two .
speaker: D
text: Oh , there is another short sample set <disfmarker>
speaker: A
text: There was another short one ,
speaker: D
text: o o sample .
speaker: A
text: yeah .
speaker: D
text: OK .
speaker: A
text: And so I haven't grabbed the latest one that he just , uh , put out yet .
speaker: D
text: Oh , OK .
speaker: D
text: F Yeah , OK .
speaker: A
text: So .
speaker: A
text: Um , but , the software seemed to compile fine and everything ,
speaker: A
text: so .
speaker: A
text: And , um ,
speaker: A
text: So .
speaker: B
text: Is there any word yet about the issues about , um , adjustments for different feature sets or anything ?
speaker: A
text: No ,
speaker: A
text: I <disfmarker> I d
speaker: A
text: You asked me to write to him
speaker: A
text: and I think I forgot to ask him about that .
speaker: B
text: Yeah .
speaker: A
text: Or if I did ask him , he didn't reply .
speaker: A
text: I <disfmarker> I don't remember yet .
speaker: A
text: Uh , I 'll <disfmarker> I 'll d I 'll double check that and ask him again .
speaker: B
text: Yeah .
speaker: D
text: Hmm .
speaker: B
text: Yeah ,
speaker: B
text: it 's like that <disfmarker> that could r turn out to be an important issue for us .
speaker: D
text: Mmm .
speaker: A
text: Yeah .
speaker: B
text: Yeah .
speaker: A
text: Yeah .
speaker: D
text: Cuz they have it <disfmarker>
speaker: A
text: Maybe I 'll send it to the list .
speaker: A
text: Yeah .
speaker: D
text: Cuz they have , uh , already frozen those in i insertion penalties and all those stuff is what <disfmarker> I feel .
speaker: D
text: Because they have this document explaining the recognizer .
speaker: A
text: Uh - huh .
speaker: D
text: And they have these tables with , uh , various language model weights , insertion penalties .
speaker: D
text: u
speaker: A
text: OK , I haven't seen that one yet .
speaker: D
text: Uh , it 's th it 's there on that web .
speaker: A
text: So .
speaker: A
text: OK .
speaker: D
text: And , uh , on that , I mean , they have run some experiments using various insertion penalties and all those <disfmarker>
speaker: A
text: And so they 've picked <disfmarker> the values .
speaker: D
text: Yeah , I think they pi p
speaker: D
text: yeah , they picked the values from <disfmarker>
speaker: A
text: Oh , OK .
speaker: A
text: OK .
speaker: B
text: For r w what test set ?
speaker: D
text: Uh , p the one that they have reported is a NIST evaluation , Wall Street Journal .
speaker: B
text: But that has nothing to do with what we 're testing on , right ?
speaker: D
text: You know .
speaker: C
text: Mm - hmm .
speaker: D
text: No .
speaker: D
text: So they 're , like <disfmarker>
speaker: D
text: um <disfmarker>
speaker: D
text: So they are actually trying to , uh , fix that <disfmarker> those values using the clean , uh , training part of the Wall Street Journal .
speaker: D
text: Which is <disfmarker>
speaker: D
text: I mean , the Aurora .
speaker: D
text: Aurora has a clean subset .
speaker: D
text: I mean , they want to train it
speaker: B
text: Right .
speaker: D
text: and then this <disfmarker> they 're going to run some evaluations .
speaker: B
text: So they 're set they 're setting it based on that ?
speaker: D
text: Yeah .
speaker: B
text: OK .
speaker: B
text: So now , we may come back to the situation where we may be looking for a modification of the features to account for the fact that we can't modify these parameters .
speaker: A
text: Yeah .
speaker: B
text: But , um ,
speaker: D
text: Yeah .
speaker: B
text: uh <disfmarker>
speaker: B
text: but it 's still worth , I think , just <disfmarker> since <disfmarker> you know , just chatting with Joe about the issue .
speaker: A
text: Yeah ,
speaker: A
text: OK .
speaker: A
text: Do you think that 's something I should just send to him
speaker: B
text: Um <disfmarker>
speaker: A
text: or do you think I should send it to this <disfmarker> there 's an <disfmarker> a m a mailing list .
speaker: B
text: Well , it 's not a secret .
speaker: B
text: I mean , we 're , you know , certainly willing to talk about it with everybody ,
speaker: B
text: but I think <disfmarker> I think that , um <disfmarker> um , it 's probably best to start talking with him just to <disfmarker>
speaker: A
text: OK .
speaker: B
text: Uh @ @ <comment> you know , it 's a dialogue between two of you about what <disfmarker> you know , what does he think about this and what <disfmarker> what <disfmarker> you know <disfmarker> what could be done about it .
speaker: A
text: Yeah .
speaker: B
text: Um ,
speaker: A
text: OK .
speaker: B
text: if you get ten people in <disfmarker> involved in it there 'll be a lot of perspectives based on , you know , how <disfmarker>
speaker: A
text: Yeah .
speaker: B
text: you know .
speaker: A
text: Right .
speaker: B
text: Uh <disfmarker>
speaker: B
text: But , I mean , I think it all should come up eventually ,
speaker: B
text: but if <disfmarker> if <disfmarker> if there is any , uh , uh , way to move in <disfmarker> a way that would <disfmarker> that would , you know , be more open to different kinds of features .
speaker: A
text: OK .
speaker: B
text: But if <disfmarker> if , uh <disfmarker> if there isn't , and it 's just kind of shut down and <disfmarker> and then also there 's probably not worthwhile bringing it into a larger forum where <disfmarker> where political issues will come in .
speaker: A
text: Yeah .
speaker: A
text: OK .
speaker: D
text: Oh .
speaker: D
text: So this is now <disfmarker> it 's <disfmarker> it 's compiled under Solaris ?
speaker: A
text: Yeah .
speaker: D
text: Yeah , OK .
speaker: D
text: Because he <disfmarker> there was some mail r saying that it 's <disfmarker> may not be stable for Linux and all those .
speaker: A
text: Yep .
speaker: A
text: Yeah .
speaker: A
text: Yeah ,
speaker: A
text: i that was a particular version .
speaker: D
text: SUSI
speaker: A
text: Yeah ,
speaker: D
text: yeah .
speaker: A
text: SUSI or whatever it was
speaker: D
text: Yeah , yeah .
speaker: A
text: but we don't have that .
speaker: D
text: Yeah , OK .
speaker: A
text: So .
speaker: D
text: OK ,
speaker: D
text: that 's fine .
speaker: A
text: Should be OK .
speaker: A
text: Yeah , it compiled fine actually .
speaker: D
text: Yeah .
speaker: A
text: No <disfmarker> no errors .
speaker: A
text: Nothing .
speaker: A
text: So .
speaker: D
text: That 's good .
speaker: B
text: Uh , this is slightly off topic
speaker: B
text: but , uh ,
speaker: B
text: I noticed , just glancing at the , uh , Hopkins workshop , uh , web site that , uh , um <disfmarker> one of the thing I don't know <disfmarker> Well , we 'll see how much they accomplish , but one of the things that they were trying to do in the graphical models thing was to put together a <disfmarker> a , uh , tool kit for doing , uh r um , arbitrary graphical models for , uh , speech recognition .
speaker: A
text: Hmm .
speaker: B
text: So <disfmarker> And Jeff , uh <disfmarker> the two Jeffs were
speaker: A
text: Who 's the second Jeff ?
speaker: B
text: Uh <disfmarker>
speaker: B
text: Oh , uh , do you know Geoff Zweig ?
speaker: A
text: No .
speaker: B
text: Oh .
speaker: B
text: Uh , he <disfmarker> he , uh <disfmarker> he was here for a couple years
speaker: B
text: and he , uh <disfmarker> got his PHD .
speaker: A
text: Oh , OK .
speaker: B
text: He <disfmarker>
speaker: B
text: And he 's , uh , been at IBM for the last couple years .
speaker: A
text: Oh , OK .
speaker: B
text: So .
speaker: A
text: Wow .
speaker: B
text: Uh , so he did <disfmarker> he did his PHD on dynamic Bayes - nets ,
speaker: A
text: That would be neat .
speaker: B
text: uh , for <disfmarker> for speech recognition .
speaker: B
text: He had some continuity built into the model ,
speaker: B
text: presumably to handle some , um , inertia in the <disfmarker> in the production system ,
speaker: B
text: and ,
speaker: B
text: um <disfmarker>
speaker: A
text: Hmm .
speaker: B
text: So .
speaker: D
text: Hmm .
speaker: C
text: Um , I 've been playing with , first , the , um , VAD .
speaker: C
text: Um , <vocalsound> so it 's exactly the same approach ,
speaker: C
text: but the features that the VAD neural network use are , uh , MFCC after noise compensation .
speaker: C
text: Oh , I think I have the results .
speaker: B
text: What was it using before ?
speaker: C
text: Before it was just P L
speaker: C
text: So .
speaker: D
text: Yeah , it was actually <disfmarker>
speaker: D
text: No .
speaker: D
text: Not <disfmarker>
speaker: D
text: I mean , it was just the noisy features I guess .
speaker: D
text: Yeah , yeah , yeah ,
speaker: C
text: Yeah ,
speaker: C
text: noisy <disfmarker> noisy features .
speaker: D
text: not compensated .
speaker: C
text: Um <disfmarker>
speaker: C
text: This is what we get after <disfmarker> This <disfmarker>
speaker: C
text: So , actually , we , yeah , here the features are noise compensated
speaker: C
text: and there is also the LDA filter .
speaker: C
text: Um , and then it 's a pretty small neural network which use , um , <vocalsound> nine frames of <disfmarker> of six features from C - zero to C - fives , plus the first derivatives .
speaker: C
text: And it has one hundred hidden units .
speaker: A
text: Is that nine frames u s uh , centered around the current frame ? Or <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: B
text: S so , I 'm <disfmarker> I 'm sorry ,
speaker: B
text: there 's <disfmarker> there 's <disfmarker> there 's how many <disfmarker> how many inputs ?
speaker: C
text: So it 's twelve times nine .
speaker: B
text: Twelve times nine inputs ,
speaker: B
text: and a hundred , uh , hidden .
speaker: C
text: Hidden
speaker: C
text: and
speaker: D
text: Two outputs .
speaker: C
text: two outputs .
speaker: B
text: Two outputs .
speaker: B
text: OK .
speaker: B
text: So I guess about eleven thousand parameters ,
speaker: C
text: Mm - hmm .
speaker: B
text: which <disfmarker> actually shouldn't be a problem , even in <disfmarker> in small phones . Yeah .
speaker: A
text: So , I 'm <disfmarker> I 'm <disfmarker> s
speaker: C
text: It should be OK .
speaker: A
text: so what is different between this and <disfmarker> and what you <disfmarker>
speaker: C
text: So the previous syst
speaker: C
text: It 's based on the system that has a fifty - three point sixty - six percent improvement .
speaker: C
text: It 's the same system .
speaker: C
text: The only thing that changed is the n a p eh <disfmarker> a es the estimation of the silence probabilities .
speaker: A
text: Ah .
speaker: A
text: OK .
speaker: C
text: Which now is based on , uh , cleaned features .
speaker: B
text: And , it 's a l it 's a lot better .
speaker: A
text: Wow .
speaker: C
text: Yeah .
speaker: C
text: Um <disfmarker>
speaker: B
text: That 's great .
speaker: C
text: So it 's <disfmarker> it 's not bad ,
speaker: C
text: but the problem is still that the latency is too large .
speaker: B
text: What 's the latency ?
speaker: C
text: Because <disfmarker>
speaker: C
text: um <disfmarker>
speaker: C
text: the <disfmarker> the latency of the VAD is two hundred and twenty milliseconds .
speaker: C
text: And , uh , the VAD is used uh , i for on - line normalization ,
speaker: C
text: and it 's used before the delta computation .
speaker: C
text: So if you add these components it goes t to a hundred and seventy ,
speaker: C
text: right ?
speaker: B
text: I <disfmarker> I 'm confused .
speaker: B
text: You started off with two - twenty and you ended up with one - seventy ?
speaker: C
text: With two an two hundred and seventy .
speaker: B
text: Two - seventy .
speaker: C
text: If <disfmarker>
speaker: C
text: Yeah ,
speaker: C
text: if you add the c delta comp delta computation
speaker: B
text: Oh .
speaker: C
text: which is done afterwards .
speaker: C
text: Um <disfmarker>
speaker: B
text: So it 's two - twenty .
speaker: B
text: I the is this <disfmarker> are these twenty - millisecond frames ?
speaker: B
text: Is that why ?
speaker: B
text: Is it after downsampling ?
speaker: C
text: The two - twenty is one hundred milliseconds for the um <disfmarker>
speaker: B
text: or <disfmarker>
speaker: C
text: No , it 's forty milliseconds for t for the , uh , uh , cleaning of the speech .
speaker: C
text: Um <disfmarker>
speaker: C
text: then there is , um , the neural network which use nine frames .
speaker: C
text: So it adds forty milliseconds .
speaker: B
text: a
speaker: B
text: OK .
speaker: C
text: Um ,
speaker: C
text: after that , um , you have the um , filtering of the silence probabilities .
speaker: C
text: Which is a million filter
speaker: C
text: it ,
speaker: C
text: and it creates a one hundred milliseconds delay .
speaker: C
text: So , um <disfmarker>
speaker: D
text: Plus there is a delta at the input .
speaker: C
text: Yeah ,
speaker: C
text: and there is the delta at the input
speaker: C
text: which is ,
speaker: B
text: One hundred milliseconds for smoothing .
speaker: C
text: um <disfmarker>
speaker: C
text: So it 's <disfmarker> @ @ <disfmarker>
speaker: B
text: Uh ,
speaker: D
text: It 's like forty plus <disfmarker> forty <disfmarker> plus <disfmarker>
speaker: B
text: median .
speaker: C
text: Mmm .
speaker: C
text: Forty <disfmarker>
speaker: B
text: And then forty <disfmarker>
speaker: C
text: This forty plus twenty , plus one hundred .
speaker: B
text: forty p
speaker: C
text: Uh <disfmarker>
speaker: D
text: So it 's two hundred actually .
speaker: C
text: Yeah , there are twenty that comes from <disfmarker>
speaker: C
text: There is ten that comes from the LDA filters also .
speaker: D
text: Oh , OK .
speaker: C
text: Right ?
speaker: C
text: Uh , so it 's two hundred and ten ,
speaker: C
text: yeah .
speaker: D
text: If you are using <disfmarker>
speaker: B
text: Uh <disfmarker>
speaker: C
text: Plus the frame ,
speaker: D
text: t If you are using three frames <disfmarker>
speaker: C
text: so it 's two - twenty .
speaker: D
text: If you are phrasing f <comment> using three frames , it is thirty here for delta .
speaker: C
text: Yeah , I think it 's <disfmarker> it 's five frames ,
speaker: D
text: So five frames , that 's twenty .
speaker: C
text: but .
speaker: D
text: OK ,
speaker: D
text: so it 's who un <comment> two hundred and ten .
speaker: B
text: Uh , p Wait a minute .
speaker: B
text: It 's forty <disfmarker> <vocalsound> forty for the <disfmarker> for the cleaning of the speech ,
speaker: C
text: So .
speaker: C
text: Forty cleaning .
speaker: B
text: forty for the I N <disfmarker> ANN ,
speaker: B
text: a hundred for the smoothing .
speaker: C
text: Yeah .
speaker: B
text: Well , but at ten <disfmarker> ,
speaker: C
text: Twenty for the delta .
speaker: D
text: At th <nonvocalsound> At the input .
speaker: B
text: Twenty for delta .
speaker: D
text: I mean , that 's at the input to the net .
speaker: C
text: Yeah .
speaker: B
text: Delta at input to net ?
speaker: D
text: And there i
speaker: C
text: Yeah .
speaker: D
text: Yeah .
speaker: D
text: So it 's like s five , six cepstrum plus delta
speaker: D
text: at nine <disfmarker> nine frames of <disfmarker>
speaker: B
text: And then ten milliseconds for <disfmarker>
speaker: D
text: Fi - There 's an LDA filter .
speaker: B
text: ten milliseconds for LDA filter ,
speaker: B
text: and t and ten <disfmarker> another ten milliseconds you said for the frame ?
speaker: C
text: For the frame I guess .
speaker: C
text: I computed two - twenty <disfmarker>
speaker: C
text: Yeah , well , it 's <disfmarker>
speaker: C
text: I guess it 's for the fr <disfmarker> the <disfmarker>
speaker: B
text: OK .
speaker: B
text: And then there 's delta besides that ?
speaker: C
text: So this is the features that are used by our network
speaker: C
text: and then afterwards , you have to compute the delta on the , uh , main feature stream ,
speaker: C
text: which is um , delta and double - deltas ,
speaker: B
text: OK .
speaker: C
text: which is fifty milliseconds .
speaker: B
text: Yeah .
speaker: B
text: No , I mean , the <disfmarker>
speaker: B
text: after the noise part , the forty <disfmarker> the <disfmarker> the other hundred and eighty <disfmarker>
speaker: B
text: Well , I mean ,
speaker: B
text: Wait a minute .
speaker: B
text: Some of this is , uh <disfmarker> is , uh <disfmarker> is in parallel , isn't it ?
speaker: B
text: I mean , the LDA <disfmarker>
speaker: B
text: Oh , you have the LDA as part of the V D - uh , VAD ?
speaker: B
text: Or <disfmarker>
speaker: C
text: The VAD use , uh , LDA filtered features also .
speaker: B
text: Oh , it does ?
speaker: C
text: Mm - hmm .
speaker: B
text: Ah .
speaker: B
text: So in that case there isn't too much in parallel .
speaker: B
text: Uh <disfmarker>
speaker: C
text: No .
speaker: C
text: There is , um , just downsampling , upsampling , and the LDA .
speaker: B
text: Um , so the delta at the end is how much ?
speaker: C
text: It 's fifty .
speaker: D
text: It 's <disfmarker>
speaker: B
text: Fifty .
speaker: B
text: Alright .
speaker: B
text: So <disfmarker>
speaker: C
text: But well , we could probably put the delta , um , <vocalsound> before on - line normalization .
speaker: C
text: It should not that make a big difference ,
speaker: A
text: What if you used a smaller window for the delta ?
speaker: C
text: because <disfmarker>
speaker: A
text: Could that help a little bit ?
speaker: A
text: I mean , I guess there 's a lot of things you could do to <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: Yeah .
speaker: C
text: Yeah ,
speaker: C
text: but , nnn <disfmarker>
speaker: B
text: So
speaker: B
text: Yeah .
speaker: B
text: So if you <disfmarker> if you put the delta before the , uh , ana on - line <disfmarker> If <disfmarker>
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah <disfmarker>
speaker: B
text: uh <disfmarker> then <disfmarker> then it could go in parallel .
speaker: C
text: Cuz i
speaker: B
text: And then y then you don't have that additive <disfmarker>
speaker: D
text: Yep .
speaker: C
text: Yeah ,
speaker: C
text: cuz the time constant of the on - line normalization is pretty long compared to the delta window ,
speaker: B
text: OK .
speaker: C
text: so .
speaker: C
text: It should not make <disfmarker>
speaker: B
text: OK .
speaker: B
text: And you ought to be able to shove tw , uh <disfmarker> sh uh <disfmarker> pull off twenty milliseconds from somewhere else to get it under two hundred ,
speaker: B
text: right ?
speaker: B
text: I mean <disfmarker>
speaker: C
text: Mm - hmm .
speaker: A
text: Is two hundred the d
speaker: B
text: The hundred milla
speaker: B
text: mill a hundred milliseconds for smoothing is sort of an arbitrary amount .
speaker: B
text: It could be eighty
speaker: B
text: and <disfmarker> and probably do @ @ <disfmarker>
speaker: C
text: Yeah ,
speaker: C
text: yeah .
speaker: A
text: i a hun
speaker: A
text: uh <disfmarker>
speaker: A
text: Wh - what 's the baseline you need to be under ?
speaker: A
text: Two hundred ?
speaker: B
text: Well , we don't know .
speaker: B
text: They 're still arguing about it .
speaker: A
text: Oh .
speaker: B
text: I mean , if it 's two <disfmarker> if <disfmarker> if it 's , uh <disfmarker> if it 's two - fifty , then we could keep the delta where it is if we shaved off twenty .
speaker: B
text: If it 's two hundred , if we shaved off twenty , we could <disfmarker> we could , uh , meet it by moving the delta back .
speaker: A
text: So , how do you know that what you have is too much if they 're still deciding ?
speaker: B
text: Uh , we don't ,
speaker: B
text: but it 's just <disfmarker>
speaker: B
text: I mean , the main thing is that since that we got burned last time , and <disfmarker> you know , by not worrying about it very much , we 're just staying conscious of it .
speaker: A
text: Uh - huh .
speaker: A
text: Oh , OK ,
speaker: A
text: I see .
speaker: B
text: And so , th
speaker: B
text: I mean , if <disfmarker> if <disfmarker> if a week before we have to be done someone says , " Well , you have to have fifty milliseconds less than you have now " , it would be pretty frantic around here .
speaker: B
text: So <disfmarker>
speaker: A
text: Ah , OK .
speaker: B
text: Uh <disfmarker>
speaker: A
text: But still , that 's <disfmarker> that 's a pretty big , uh , win .
speaker: A
text: And it doesn't seem like you 're <disfmarker> in terms of your delay , you 're , uh , that <disfmarker>
speaker: B
text: He added a bit on ,
speaker: C
text: Hmm .
speaker: B
text: I guess , because before we were <disfmarker> we were <disfmarker> had <disfmarker> were able to have the noise , uh , stuff , uh , and the LVA be in parallel .
speaker: B
text: And now he 's <disfmarker> he 's requiring it to be done first .
speaker: C
text: Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so .
speaker: B
text: Right .
speaker: B
text: Well , so you say <disfmarker>
speaker: C
text: And <disfmarker>
speaker: B
text: let 's say ten milliseconds <disfmarker> seconds for the LDA .
speaker: C
text: and <disfmarker> but <disfmarker> the LDA is , well , pretty short right now .
speaker: B
text: Well , ten .
speaker: C
text: Yeah .
speaker: B
text: And then forty for the other .
speaker: D
text: Yeah , the LDA <disfmarker> LDA <disfmarker> we don't know , is , like <disfmarker> is it very crucial for the features , right ?
speaker: C
text: No .
speaker: C
text: I just <disfmarker>
speaker: D
text: Yeah .
speaker: C
text: This is the first try .
speaker: C
text: I mean , I <disfmarker> maybe the LDA 's not very useful then .
speaker: B
text: Right ,
speaker: D
text: S s h
speaker: B
text: so you could start pulling back ,
speaker: B
text: but <disfmarker>
speaker: D
text: Yeah ,
speaker: D
text: l
speaker: B
text: But I think you have <disfmarker>
speaker: B
text: I mean , you have twenty for delta computation
speaker: B
text: which y now you 're sort of doing twice ,
speaker: B
text: right ?
speaker: B
text: But yo w were you doing that before ?
speaker: C
text: Mmm .
speaker: D
text: On the <disfmarker> in the <disfmarker>
speaker: C
text: Well , in the proposal , um , the input of the VAD network were just three frames , I think .
speaker: D
text: Mm - hmm .
speaker: D
text: Just <disfmarker>
speaker: D
text: Yeah , just the static , no delta .
speaker: C
text: Uh , static features .
speaker: B
text: Right .
speaker: B
text: So , what you have now is fort uh , forty for the <disfmarker> the noise , twenty for the delta , and ten for the LDA .
speaker: B
text: That 's seventy milliseconds of stuff which was formerly in parallel ,
speaker: B
text: right ?
speaker: B
text: So I think ,
speaker: C
text: Mm - hmm .
speaker: B
text: you know , that 's <disfmarker> that 's the difference as far as the timing ,
speaker: C
text: Yeah .
speaker: B
text: right ?
speaker: B
text: Um ,
speaker: B
text: and you could experiment with cutting various pieces of these back a bit ,
speaker: B
text: but <disfmarker>
speaker: B
text: I mean , we 're s we 're not <disfmarker> we 're not in terrible shape .
speaker: A
text: Yeah ,
speaker: A
text: that 's what it seems like to me .
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: A
text: It 's pretty good .
speaker: B
text: It 's <disfmarker> it 's not like it 's adding up to four hundred milliseconds or something .
speaker: A
text: Where <disfmarker> where is this <disfmarker> where is this fifty - seven point O two in <disfmarker> in comparison to the last evaluation ?
speaker: B
text: Well , it 's <disfmarker> I think it 's better than anything , uh , anybody got .
speaker: C
text: Yeah .
speaker: A
text: Oh , is that right ?
speaker: C
text: The best was fifty - four point five .
speaker: B
text: Yeah .
speaker: D
text: Point s
speaker: A
text: Oh .
speaker: B
text: Yeah .
speaker: C
text: And our system was forty - nine ,
speaker: B
text: Uh
speaker: C
text: but with the neural network .
speaker: A
text: Wow .
speaker: A
text: So this is almost ten percent .
speaker: B
text: With the f with the neural net .
speaker: B
text: Yeah ,
speaker: D
text: Yeah ,
speaker: B
text: and r and <disfmarker>
speaker: C
text: It would
speaker: D
text: so this is <disfmarker> this is like the first proposal .
speaker: D
text: The proposal - one .
speaker: D
text: It was forty - four , actually .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: And we still don't have the neural net in .
speaker: B
text: So <disfmarker> so it 's <disfmarker>
speaker: A
text: Wow .
speaker: B
text: You know .
speaker: B
text: So it 's <disfmarker>
speaker: B
text: We 're <disfmarker> we 're doing better .
speaker: B
text: I mean , we 're getting better recognition .
speaker: A
text: This is <disfmarker> this is really good .
speaker: B
text: I mean , I 'm sure other people working on this are not sitting still either ,
speaker: B
text: but <disfmarker>
speaker: B
text: but <disfmarker>
speaker: A
text: Yeah .
speaker: B
text: but , uh <disfmarker>
speaker: B
text: Uh ,
speaker: B
text: I mean , the important thing is that we learn how to do this better ,
speaker: B
text: and , you know .
speaker: B
text: So .
speaker: B
text: Um ,
speaker: B
text: Yeah .
speaker: B
text: So , our ,
speaker: B
text: um <disfmarker>
speaker: B
text: Yeah , you can see the kind of <disfmarker> kind of numbers that we 're having , say , on SpeechDat - Car
speaker: B
text: which is a hard task ,
speaker: B
text: cuz it 's really , um <disfmarker> I think it 's just sort of <disfmarker> sort of reasonable numbers , starting to be .
speaker: C
text: Mm - hmm .
speaker: B
text: I mean , it 's still terri
speaker: C
text: Yeah , even for a well - matched case it 's sixty percent error rate reduction ,
speaker: C
text: which is <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: Probably half .
speaker: B
text: Good !
speaker: C
text: Um ,
speaker: C
text: Yeah .
speaker: C
text: So actually , this is in between <vocalsound> what we had with the previous VAD and what Sunil did with an IDL VAD .
speaker: C
text: Which gave sixty - two percent improvement , right ?
speaker: D
text: Yeah , it 's almost that .
speaker: C
text: So <disfmarker>
speaker: D
text: It 's almost an average
speaker: D
text: somewhere around <disfmarker>
speaker: C
text: Yeah .
speaker: D
text: Yeah .
speaker: A
text: What was that ?
speaker: A
text: Say that last part again ?
speaker: C
text: So , if you use , like , an IDL VAD , uh , for dropping the frames ,
speaker: D
text: o o
speaker: D
text: Or the best we can get .
speaker: C
text: the best that we can get <disfmarker> i That means that we estimate the silence probability on the clean version of the utterances .
speaker: C
text: Then you can go up to sixty - two percent error rate reduction , globally .
speaker: A
text: Mmm .
speaker: C
text: Mmm <disfmarker>
speaker: C
text: Yeah .
speaker: A
text: So that would be even <disfmarker> That wouldn't change this number down here to sixty - two ?
speaker: C
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: So you <disfmarker> you were get
speaker: C
text: If you add a g good v very good VAD , that works as well as a VAD working on clean speech ,
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: C
text: then you wou you would go <disfmarker>
speaker: A
text: So that 's sort of the best you could hope for .
speaker: C
text: Mm - hmm .
speaker: B
text: Probably . Yeah .
speaker: A
text: I see .
speaker: B
text: So fi si fifty - three is what you were getting with the old VAD .
speaker: C
text: Yeah .
speaker: B
text: And , uh <disfmarker>
speaker: B
text: and sixty - two with the <disfmarker> the , you know , quote , unquote , cheating VAD .
speaker: B
text: And fifty - seven is what you got with the real VAD .
speaker: C
text: Mm - hmm .
speaker: B
text: That 's great .
speaker: C
text: Uh , yeah , the next thing is , I started to play <disfmarker>
speaker: C
text: Well , I don't want to worry too much about the delay ,
speaker: C
text: no .
speaker: C
text: Maybe it 's better to wait
speaker: B
text: OK .
speaker: C
text: for the decision
speaker: B
text: Yeah .
speaker: C
text: from the committee .
speaker: C
text: Uh , but I started to play with the , um , <vocalsound> <vocalsound> uh , tandem neural network .
speaker: C
text: Mmm
speaker: C
text: I just did the configuration that 's very similar to what we did for the February proposal .
speaker: C
text: And <disfmarker>
speaker: C
text: Um .
speaker: C
text: So . There is a f a first feature stream that use uh straight MFCC features .
speaker: B
text: Mm - hmm .
speaker: C
text: Well , these features actually .
speaker: C
text: And the other stream is the output of a neural network , using as input , also , these , um , cleaned MFCC .
speaker: C
text: Um <disfmarker>
speaker: C
text: I don't have the comp
speaker: A
text: Those are th those are th what is going into the tandem net ?
speaker: C
text: Mmm ?
speaker: A
text: Those two ?
speaker: C
text: So there is just this feature stream , <comment> the fifteen MFCC plus delta and double - delta .
speaker: B
text: No .
speaker: A
text: Yeah ?
speaker: C
text: Um , so it 's <disfmarker> makes forty - five features <comment> that are used as input to the HTK .
speaker: C
text: And then , there is <disfmarker> there are more inputs that comes from the tandem MLP .
speaker: A
text: Oh , oh .
speaker: A
text: OK .
speaker: B
text: Yeah ,
speaker: A
text: I see .
speaker: B
text: h he likes to use them both ,
speaker: B
text: cuz then it has one part that 's discriminative ,
speaker: A
text: Uh - huh .
speaker: C
text: Yeah .
speaker: C
text: Um <disfmarker>
speaker: B
text: one part that 's not .
speaker: A
text: Right .
speaker: A
text: OK .
speaker: C
text: So , um ,
speaker: C
text: uh , yeah .
speaker: C
text: Right now it seems that <disfmarker> i I just tested on SpeechDat - Car while the experiment are running on your <disfmarker> on TI - digits .
speaker: C
text: Well , it improves on the well - matched and the mismatched conditions ,
speaker: C
text: but it get worse on the highly mismatched .
speaker: C
text: Um ,
speaker: A
text: Compared to these numbers ?
speaker: C
text: Compared to these numbers , yeah .
speaker: C
text: Um ,
speaker: C
text: like , on the well - match and medium mismatch , the gain is around five percent relative ,
speaker: B
text: y
speaker: C
text: but it goes down a lot more , like fifteen percent on the HM case .
speaker: B
text: You 're just using the full ninety features ?
speaker: C
text: The <disfmarker>
speaker: B
text: Y you have ninety features ?
speaker: C
text: i
speaker: C
text: I have ,
speaker: C
text: um <disfmarker>
speaker: C
text: From the networks , it 's twenty - eight .
speaker: C
text: So <disfmarker>
speaker: B
text: And from the other side it 's forty - five .
speaker: C
text: So , d i It 's forty - five .
speaker: B
text: So it 's <disfmarker> you have seventy - three features ,
speaker: C
text: Yeah .
speaker: B
text: and you 're just feeding them like that .
speaker: C
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: B
text: There isn't any KLT or anything ?
speaker: C
text: There 's a KLT after the neural network , as <disfmarker> as before .
speaker: A
text: That 's how you get down to twenty - eight ?
speaker: C
text: Yeah .
speaker: A
text: Why twenty - eight ?
speaker: C
text: I don't know .
speaker: C
text: Uh .
speaker: A
text: Oh .
speaker: C
text: It 's <disfmarker> i i i It 's because it 's what we did for the first proposal .
speaker: C
text: We tested , uh , trying to go down
speaker: A
text: Ah .
speaker: B
text: It 's a multiple of seven .
speaker: C
text: and
speaker: D
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: So <disfmarker>
speaker: C
text: Um .
speaker: D
text: Yeah .
speaker: C
text: I wanted to do something very similar to the proposal as a first <disfmarker> first try .
speaker: D
text: Yeah .
speaker: A
text: I see .
speaker: B
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: That makes sense .
speaker: C
text: But we have to <disfmarker> for sure , we have to go down ,
speaker: C
text: because the limit is now sixty features .
speaker: C
text: So ,
speaker: B
text: Yeah .
speaker: C
text: uh ,
speaker: C
text: we have to find a way to decrease the number of features .
speaker: C
text: Um <disfmarker>
speaker: A
text: So , it seems funny that <disfmarker> I don't know , maybe I don't u quite understand everything , <comment> but that adding features <disfmarker>
speaker: A
text: I guess <disfmarker> I guess if you 're keeping the back - end fixed .
speaker: A
text: Maybe that 's it .
speaker: A
text: Because it seems like just adding information shouldn't give worse results .
speaker: A
text: But I guess if you 're keeping the number of Gaussians fixed in the recognizer , then <disfmarker>
speaker: B
text: Well , yeah .
speaker: C
text: Mmm .
speaker: B
text: But , I mean , just in general , adding information <disfmarker>
speaker: B
text: Suppose the information you added , well , was a really terrible feature and all it brought in was noise .
speaker: A
text: Yeah .
speaker: B
text: Right ?
speaker: B
text: So <disfmarker> so , um <disfmarker>
speaker: B
text: Or <disfmarker> or suppose it wasn't completely terrible ,
speaker: B
text: but it was completely equivalent to another one feature that you had ,
speaker: B
text: except it was noisier .
speaker: A
text: Uh - huh .
speaker: B
text: Right ?
speaker: B
text: In that case you wouldn't necessarily expect it to be better at all .
speaker: A
text: Oh , yeah , I wasn't necessarily saying it should be better .
speaker: A
text: I 'm just surprised that you 're getting fifteen percent relative worse on the wel
speaker: B
text: Uh - huh .
speaker: C
text: But it 's worse .
speaker: B
text: On the highly mismatched condition .
speaker: A
text: On the highly mismatch .
speaker: C
text: Yeah , I <disfmarker>
speaker: A
text: Yeah .
speaker: B
text: So , " highly mismatched condition " means that in fact your training is a bad estimate of your test .
speaker: C
text: Uh - huh .
speaker: B
text: So having <disfmarker> having , uh , a g a l a greater number of features , if they aren't maybe the right features that you use , certainly can e can easily , uh , make things worse .
speaker: B
text: I mean , you 're right .
speaker: B
text: If you have <disfmarker> if you have , uh , lots and lots of data , and you have <disfmarker> and your <disfmarker> your <disfmarker> your training is representative of your test , then getting more sources of information should just help .
speaker: B
text: But <disfmarker> but it 's <disfmarker> It doesn't necessarily work that way .
speaker: A
text: Huh .
speaker: C
text: Mm - hmm .
speaker: B
text: So I wonder ,
speaker: B
text: um ,
speaker: B
text: Well , what 's your <disfmarker> what 's your thought about what to do next with it ?
speaker: C
text: Um ,
speaker: C
text: I don't know .
speaker: C
text: I 'm surprised ,
speaker: C
text: because I expected the neural net to help more when there is more mismatch , as it was the case for the <disfmarker>
speaker: B
text: Mm - hmm .
speaker: D
text: So , was the training set same as the p the February proposal ?
speaker: D
text: OK .
speaker: C
text: Yeah , it 's the same training set ,
speaker: C
text: so it 's TIMIT with the TI - digits ' , uh , noises , uh , added .
speaker: B
text: Mm - hmm .
speaker: C
text: Um <disfmarker>
speaker: B
text: Well , we might <disfmarker> uh , we might have to experiment with , uh better training sets .
speaker: B
text: Again .
speaker: C
text: Mm - hmm .
speaker: B
text: But ,
speaker: B
text: I <disfmarker> The other thing is , I mean , before you found that was the best configuration , but you might have to retest those things now that we have different <disfmarker> The rest of it is different ,
speaker: B
text: right ?
speaker: B
text: So ,
speaker: B
text: um ,
speaker: B
text: uh ,
speaker: B
text: For instance , what 's the effect of just putting the neural net on without the o other <disfmarker> other path ?
speaker: C
text: Mm - hmm .
speaker: B
text: I mean , you know what the straight features do .
speaker: C
text: Yeah .
speaker: B
text: That gives you this .
speaker: C
text: Mm - hmm .
speaker: B
text: You know what it does in combination .
speaker: B
text: You don't necessarily know what <disfmarker>
speaker: A
text: What if you did the <disfmarker>
speaker: A
text: Would it make sense to do the KLT on the full set of combined features ?
speaker: A
text: Instead of just on the <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: I g I guess .
speaker: C
text: Um .
speaker: C
text: The reason I did it this ways is that in February , it <disfmarker> we <disfmarker> we tested different things like that ,
speaker: C
text: so , having two KLT , having just a KLT for a network , or having a global KLT .
speaker: A
text: Oh , I see .
speaker: C
text: And <disfmarker>
speaker: A
text: So you tried the global KLT before
speaker: C
text: Well <disfmarker>
speaker: C
text: Yeah .
speaker: A
text: and it didn't really <disfmarker>
speaker: C
text: And , uh , th
speaker: C
text: Yeah .
speaker: C
text: The differences between these configurations were not huge ,
speaker: A
text: I see .
speaker: C
text: but <disfmarker> it was marginally better with this configuration .
speaker: A
text: Uh - huh .
speaker: A
text: Uh - huh .
speaker: B
text: But , yeah , that 's obviously another thing to try ,
speaker: C
text: Um .
speaker: B
text: since things are <disfmarker> things are different .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: And I guess if the <disfmarker>
speaker: B
text: These are all <disfmarker>
speaker: B
text: so all of these seventy - three features are going into , um , the , uh <disfmarker> the HMM .
speaker: C
text: Yeah .
speaker: B
text: And is <disfmarker> are <disfmarker> i i are <disfmarker> are any deltas being computed of tha of them ?
speaker: C
text: Of the straight features , yeah .
speaker: C
text: So .
speaker: B
text: n Not of the <disfmarker>
speaker: C
text: But n th the , um ,
speaker: C
text: tandem features are u used as they are .
speaker: B
text: Are not .
speaker: C
text: So ,
speaker: C
text: yeah , maybe we can add some context from these features also as <disfmarker> Dan did in <disfmarker> in his last work .
speaker: B
text: Could .
speaker: B
text: i
speaker: B
text: Yeah ,
speaker: B
text: but the other thing I was thinking was ,
speaker: B
text: um <disfmarker>
speaker: B
text: Uh , now I lost track of what I was thinking .
speaker: B
text: But .
speaker: A
text: What is the <disfmarker>
speaker: A
text: You said there was a limit of sixty features or something ?
speaker: C
text: Mm - hmm .
speaker: A
text: What 's the relation between that limit and the , um , forty - eight <disfmarker> uh , forty eight hundred bits per second ?
speaker: B
text: Oh ,
speaker: B
text: I know what I was gonna say .
speaker: C
text: Um , not <disfmarker> no relation .
speaker: B
text: No relation .
speaker: A
text: So I <disfmarker> I <disfmarker> I don't understand ,
speaker: C
text: The f the forty - eight hundred bits is for transmission of some features .
speaker: A
text: because i
speaker: A
text: I mean , if you 're only using h
speaker: C
text: And generally , i it <disfmarker> s allows you to transmit like , fifteen , uh , cepstrum .
speaker: B
text: The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere
speaker: B
text: which might be , you know , it <disfmarker> it might be a concern how many parameters are use <disfmarker> u used and so forth .
speaker: B
text: And so ,
speaker: B
text: uh ,
speaker: B
text: they felt they wanted to set a limit .
speaker: B
text: So they chose sixty .
speaker: B
text: Some people wanted to use hundreds of parameters
speaker: B
text: and <disfmarker> and that bothered some other people .
speaker: B
text: u And so
speaker: A
text: Uh - huh .
speaker: B
text: they just chose that .
speaker: B
text: I <disfmarker> I <disfmarker> I think it 's kind of r arbitrary too .
speaker: B
text: But <disfmarker> but that 's <disfmarker> that 's kind of what was chosen .
speaker: B
text: I <disfmarker> I remembered what I was going to say .
speaker: B
text: What I was going to say is that , um , maybe <disfmarker> <vocalsound> maybe with the noise removal , uh , these things are now more correlated .
speaker: B
text: So you have two sets of things that are kind of uncorrelated , uh , within themselves ,
speaker: B
text: but they 're pretty correlated with one another .
speaker: C
text: Mm - hmm .
speaker: B
text: And , um ,
speaker: B
text: they 're being fed into these , uh , variants , only Gaussians and so forth ,
speaker: B
text: and <disfmarker> and , uh ,
speaker: C
text: Mm - hmm .
speaker: B
text: so maybe it would be a better idea now than it was before to , uh , have , uh , one KLT over everything ,
speaker: C
text: Mm - hmm .
speaker: B
text: to de - correlate it .
speaker: C
text: Yeah , I see .
speaker: B
text: Maybe .
speaker: B
text: You know .
speaker: D
text: What are the S N Rs in the training set , TIMIT ?
speaker: C
text: It 's , uh , ranging from zero to clean ?
speaker: C
text: Yeah .
speaker: D
text: Mm - hmm .
speaker: C
text: From zero to clean .
speaker: B
text: Yeah .
speaker: B
text: So we found this <disfmarker> this , uh <disfmarker> this Macrophone data , and so forth , that we were using for these other experiments , to be pretty good .
speaker: C
text: Mm - hmm .
speaker: B
text: So that 's <disfmarker> i after you explore these other alternatives , that might be another way to start looking , is <disfmarker> is just improving the training set .
speaker: C
text: Mm - hmm .
speaker: B
text: I mean , we were getting , uh , lots better recognition using that , than <disfmarker>
speaker: B
text: Of course , you do have the problem that , um , u i <comment> we are not able to increase the number of Gaussians , uh , or anything to , uh , uh , to match anything .
speaker: B
text: So we 're only improving the training of our feature set ,
speaker: B
text: but that 's still probably something .
speaker: A
text: So you 're saying , add the Macrophone data to the training of the neural net ? The tandem net ?
speaker: B
text: Yeah ,
speaker: B
text: that 's the only place that we can train .
speaker: B
text: We can't train the other stuff with anything other than the standard amount ,
speaker: A
text: Yeah .
speaker: A
text: Right .
speaker: B
text: so .
speaker: B
text: Um ,
speaker: B
text: um <disfmarker>
speaker: A
text: What <disfmarker> what was it trained on again ?
speaker: A
text: The one that you used ?
speaker: C
text: It 's TIMIT with noise .
speaker: A
text: Uh - huh .
speaker: C
text: So , yeah , it 's rather a small <disfmarker>
speaker: B
text: Yeah .
speaker: C
text: Um ,
speaker: B
text: How big is the net , by the way ?
speaker: C
text: Uh , it 's , uh , five hundred hidden units .
speaker: C
text: And <disfmarker>
speaker: B
text: And again , you did experiments back then where you made it bigger
speaker: B
text: and it <disfmarker> and that was <disfmarker> that was sort of the threshold point .
speaker: B
text: Much less than that , it was worse ,
speaker: C
text: Yeah .
speaker: B
text: and
speaker: C
text: Yeah .
speaker: B
text: much more than that , it wasn't much better .
speaker: B
text: Hmm .
speaker: D
text: So is it <disfmarker> is it though the performance , big relation in the high ma high mismatch has something to do with the , uh , cleaning up that you <disfmarker> that is done on the TIMIT after adding noise ?
speaker: C
text: Yeah . @ @ ?
speaker: D
text: So <disfmarker>
speaker: D
text: it 's <disfmarker> i All the noises are from the TI - digits ,
speaker: D
text: right ?
speaker: C
text: Yeah .
speaker: D
text: So you <disfmarker> i
speaker: C
text: Um <disfmarker>
speaker: D
text: Well , it it 's like the high mismatch of the SpeechDat - Car
speaker: C
text: They <disfmarker> k uh <disfmarker>
speaker: D
text: after cleaning up , maybe having more noise than the <disfmarker> the training set of TIMIT after clean <disfmarker> s after you do the noise clean - up .
speaker: C
text: Mmm .
speaker: D
text: I mean , earlier you never had any compensation ,
speaker: D
text: you just trained it straight away .
speaker: C
text: Mm - hmm .
speaker: D
text: So it had like all these different conditions of S N Rs , actually in their training set of neural net .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: D
text: But after cleaning up you have now a different set of S N Rs , right ?
speaker: C
text: Yeah .
speaker: D
text: For the training of the neural net .
speaker: C
text: Mm - hmm .
speaker: D
text: And <disfmarker>
speaker: D
text: is it something to do with the mismatch that <disfmarker> that 's created after the cleaning up , like the high mismatch <disfmarker>
speaker: C
text: You mean the <disfmarker> the most noisy occurrences on SpeechDat - Car might be a lot more noisy than <disfmarker>
speaker: D
text: Mm - hmm .
speaker: D
text: Of <disfmarker> that <disfmarker>
speaker: D
text: I mean , the SNR after the noise compensation of the SpeechDat - Car .
speaker: B
text: Oh ,
speaker: B
text: so <disfmarker> Right .
speaker: B
text: So the training <disfmarker> the <disfmarker> the neural net is being trained with noise compensated stuff .
speaker: C
text: Maybe .
speaker: D
text: Yeah .
speaker: C
text: Yeah ,
speaker: C
text: yeah .
speaker: D
text: Yeah .
speaker: B
text: Which makes sense ,
speaker: B
text: but , uh , you 're saying <disfmarker> Yeah , the noisier ones are still going to be , even after our noise compensation , are still gonna be pretty noisy .
speaker: D
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: D
text: Yeah , so now the after - noise compensation the neural net is seeing a different set of S N Rs than that was originally there in the training set . Of TIMIT .
speaker: D
text: Because in the TIMIT it was zero to some clean .
speaker: B
text: Right .
speaker: B
text: Yes .
speaker: D
text: So the net saw all the SNR @ @ conditions .
speaker: B
text: Right .
speaker: D
text: Now after cleaning up it 's a different set of SNR .
speaker: B
text: Right .
speaker: D
text: And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car .
speaker: B
text: Right ,
speaker: B
text: but the SpeechDat - Car data that you 're seeing is also reduced in noise
speaker: D
text: Yeah , yeah , yeah ,
speaker: C
text: Yeah .
speaker: B
text: by the noise compensation .
speaker: D
text: yeah , it is .
speaker: D
text: But , I 'm saying , there could be some <disfmarker> some issues of <disfmarker>
speaker: B
text: So .
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: C
text: Well , if the initial range of SNR is different , we <disfmarker> the problem was already there before .
speaker: C
text: And <disfmarker>
speaker: B
text: Yeah .
speaker: C
text: Because <disfmarker>
speaker: C
text: Mmm <disfmarker>
speaker: B
text: Yeah , I mean , it depends on whether you believe that the noise compensation is equally reducing the noise on the test set and the training set .
speaker: C
text: Hmm .
speaker: D
text: On the test set , yeah .
speaker: B
text: Uh <disfmarker>
speaker: B
text: Right ?
speaker: B
text: I mean , you 're saying there 's a mismatch in noise that wasn't there before ,
speaker: D
text: Hmm .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: but if they were both the same before , then if they were both reduic reduced equally , then , there would not be a mismatch .
speaker: B
text: So ,
speaker: B
text: I mean , this may be <disfmarker>
speaker: B
text: Heaven forbid , this noise compensation process may be imperfect ,
speaker: B
text: but .
speaker: B
text: Uh ,
speaker: C
text: Yeah , uh <disfmarker>
speaker: D
text: Well , I <disfmarker>
speaker: B
text: so maybe it 's treating some things differently .
speaker: D
text: I don't know .
speaker: D
text: I <disfmarker> I just <disfmarker> that could be seen from the TI - digits , uh , testing condition
speaker: D
text: because , um , the noises are from the TI - digits , right ?
speaker: D
text: Noise <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: So <disfmarker>
speaker: D
text: So cleaning up the TI - digits
speaker: D
text: and if the performance goes down in the TI - digits mismatch <disfmarker> high mismatch like this <disfmarker>
speaker: C
text: Clean training , yeah .
speaker: D
text: on a clean training , or zero DB testing .
speaker: C
text: Yeah , we 'll <disfmarker> so we 'll see .
speaker: D
text: Yeah .
speaker: C
text: Uh .
speaker: D
text: Then it 's something to do .
speaker: C
text: Maybe .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: B
text: I mean , one of the things about <disfmarker>
speaker: B
text: I mean , the Macrophone data , um , I think , you know , it was recorded over many different telephones .
speaker: C
text: Mm - hmm .
speaker: B
text: And , um ,
speaker: B
text: so , there 's lots of different kinds of acoustic conditions .
speaker: B
text: I mean , it 's not artificially added noise or anything .
speaker: B
text: So it 's not the same .
speaker: B
text: I don't think there 's anybody recording over a car from a car ,
speaker: B
text: but <disfmarker> I think it 's <disfmarker> it 's varied enough that if <disfmarker> if doing this adjustments , uh , and playing around with it doesn't , uh , make it better , the most <disfmarker> uh , it seems like the most obvious thing to do is to improve the training set .
speaker: B
text: Um <disfmarker>
speaker: B
text: I mean , what we were <disfmarker>
speaker: B
text: uh <disfmarker> the condition <disfmarker> It <disfmarker> it gave us an enormous amount of improvement in what we were doing with Meeting Recorder digits ,
speaker: B
text: even though there , again , these m Macrophone digits were very , very different from , uh , what we were going on here .
speaker: B
text: I mean , we weren't talking over a telephone here .
speaker: B
text: But it was just <disfmarker> I think just having a <disfmarker> a nice variation in acoustic conditions was just a good thing .
speaker: C
text: Mm - hmm .
speaker: C
text: Yep .
speaker: D
text: Mmm .
speaker: C
text: Yeah , actually <vocalsound> to s eh , what I observed in the HM case is that the number of deletion dramatically increases .
speaker: C
text: It <disfmarker> it doubles .
speaker: B
text: Number of deletions .
speaker: C
text: When I added the num the neural network it doubles the number of deletions .
speaker: C
text: Yeah , so I don't you know <vocalsound> how to interpret that ,
speaker: C
text: but , mmm <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: Me either .
speaker: C
text: t
speaker: A
text: And <disfmarker> and did <disfmarker> an other numbers stay the same ?
speaker: A
text: Insertion substitutions stay the same ?
speaker: C
text: They p stayed the same ,
speaker: C
text: they <disfmarker> maybe they are a little bit uh , lower .
speaker: A
text: Roughly ?
speaker: A
text: Uh - huh .
speaker: C
text: They are a little bit better .
speaker: C
text: Yeah . But <disfmarker>
speaker: C
text: Mm - hmm .
speaker: B
text: Did they increase the number of deletions even for the cases that got better ?
speaker: B
text: Say , for the <disfmarker> I mean , it <disfmarker>
speaker: C
text: No , it doesn't .
speaker: C
text: No .
speaker: B
text: So it 's only the highly mismatched ?
speaker: B
text: And it <disfmarker> Remind me again ,
speaker: B
text: the " highly mismatched " means that the <disfmarker>
speaker: C
text: Clean training and <disfmarker>
speaker: B
text: Uh , sorry ?
speaker: C
text: It 's clean training <disfmarker>
speaker: C
text: Well , close microphone training and distant microphone , um , high speed , I think .
speaker: B
text: Close mike training <disfmarker>
speaker: C
text: Well <disfmarker>
speaker: C
text: The most noisy cases are the distant microphone for testing .
speaker: B
text: Right .
speaker: B
text: So <disfmarker>
speaker: B
text: Well , maybe the noise subtraction is subtracting off speech .
speaker: C
text: Separating .
speaker: C
text: Yeah .
speaker: C
text: But <disfmarker>
speaker: B
text: Wh
speaker: C
text: Yeah .
speaker: C
text: I mean , but without the neural network it 's <disfmarker> well , it 's better .
speaker: C
text: It 's just when we add the neural networks .
speaker: C
text: The feature are the same except that <disfmarker>
speaker: B
text: Yeah , right .
speaker: B
text: Uh , that 's right , that 's right .
speaker: B
text: Um <disfmarker>
speaker: A
text: Well that <disfmarker> that says that , you know , the , um <disfmarker> the models in <disfmarker> in , uh , the recognizer are really paying attention to the neural net features .
speaker: C
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: A
text: Uh .
speaker: B
text: But , yeah ,
speaker: B
text: actually <disfmarker> <nonvocalsound> the TIMIT noises <pause> are sort of a range of noises
speaker: B
text: and they 're not so much the stationary driving kind of noises , right ?
speaker: B
text: It 's <disfmarker> it 's pretty different .
speaker: B
text: Isn't it ?
speaker: C
text: Uh , there is a car noise .
speaker: C
text: So there are f just four noises .
speaker: C
text: Um ,
speaker: C
text: uh , " Car " , I think ,
speaker: D
text: " Babble . "
speaker: C
text: " Babble " ,
speaker: C
text: " Subway " , right ?
speaker: C
text: and <disfmarker>
speaker: D
text: " Street " or " Airport " or something .
speaker: C
text: and <disfmarker> " Street " isn't <disfmarker>
speaker: D
text: Or " Train station " .
speaker: C
text: " Train station " , yeah .
speaker: D
text: Yeah .
speaker: C
text: So <disfmarker> it 's mostly <disfmarker> Well , " Car " is stationary ,
speaker: B
text: Mm - hmm .
speaker: C
text: " Babble " , it 's a stationary background plus some voices ,
speaker: B
text: Mm - hmm .
speaker: C
text: some speech over it .
speaker: C
text: And the other two are rather stationary also .
speaker: B
text: Well , I <disfmarker> I think that if you run it <disfmarker>
speaker: B
text: Actually , you <disfmarker> maybe you remember this .
speaker: B
text: When you <disfmarker> in <disfmarker> in the old experiments when you ran with the neural net only , and didn't have this side path , um , uh , with the <disfmarker> the pure features as well , did it make things better to have the neural net ?
speaker: C
text: Mm - hmm .
speaker: B
text: Was it about the same ?
speaker: B
text: Uh , w i
speaker: C
text: It was <disfmarker> b a little bit worse .
speaker: B
text: Than <disfmarker> ?
speaker: C
text: Than just the features ,
speaker: C
text: yeah .
speaker: B
text: So ,
speaker: B
text: until you put the second path in with the pure features , the neural net wasn't helping at all .
speaker: C
text: Mm - hmm .
speaker: B
text: Well , that 's interesting .
speaker: C
text: It was helping , uh , if the features are b were bad ,
speaker: C
text: I mean .
speaker: B
text: Yeah .
speaker: C
text: Just plain P L Ps or M F
speaker: B
text: Yeah .
speaker: C
text: C Cs .
speaker: C
text: as soon as we added LDA on - line normalization , and <vocalsound> all these things , then <disfmarker>
speaker: B
text: They were doing similar enough things .
speaker: B
text: Well , I still think it would be k sort of interesting to see what would happen if you just had the neural net without the side thing .
speaker: C
text: Yeah ,
speaker: C
text: mm - hmm .
speaker: B
text: And <disfmarker> and the thing I <disfmarker> I have in mind is , uh , maybe you 'll see that the results are not just a little bit worse .
speaker: B
text: Maybe that they 're a lot worse .
speaker: B
text: You know ?
speaker: B
text: And , um <disfmarker>
speaker: B
text: But if on the ha other hand , uh , it 's , say , somewhere in between what you 're seeing now and <disfmarker> and <disfmarker> and , uh , what you 'd have with just the pure features , then maybe there is some problem of a <disfmarker> of a , uh , combination of these things , or correlation between them somehow .
speaker: C
text: Mm - hmm .
speaker: B
text: If it really is that the net is hurting you at the moment , then I think the issue is to focus on <disfmarker> on , uh , improving the <disfmarker> the net .
speaker: C
text: Yeah ,
speaker: C
text: mm - hmm .
speaker: B
text: Um .
speaker: B
text: So what 's the overall effe
speaker: B
text: I mean , you haven't done all the experiments
speaker: B
text: but you said it was i somewhat better , say , five percent better , for the first two conditions , and fifteen percent worse for the other one ?
speaker: B
text: But it 's <disfmarker> but of course that one 's weighted lower ,
speaker: C
text: Y yeah , oh .
speaker: C
text: Yeah .
speaker: B
text: so I wonder what the net effect is .
speaker: C
text: I d I <disfmarker> I think it 's <disfmarker> it was one or two percent .
speaker: C
text: That 's not that bad ,
speaker: C
text: but it was l like two percent relative worse on SpeechDat - Car .
speaker: C
text: I have to <disfmarker> to check that .
speaker: C
text: Well , I have <disfmarker> I will .
speaker: D
text: Well , it will <disfmarker> overall it will be still better
speaker: D
text: even if it is fifteen percent worse ,
speaker: D
text: because the fifteen percent worse is given like f w twenty - five <disfmarker> point two five eight .
speaker: B
text: Right .
speaker: C
text: Mm - hmm .
speaker: C
text: Hmm .
speaker: B
text: Right .
speaker: B
text: So the <disfmarker> so the worst it could be , if the others were exactly the same , is four ,
speaker: D
text: Is it like <disfmarker>
speaker: D
text: Yeah , so it 's four .
speaker: B
text: and <disfmarker> and , uh , in fact since the others are somewhat better <disfmarker>
speaker: D
text: Is i
speaker: D
text: So either it 'll get cancelled out , or you 'll get , like , almost the same .
speaker: C
text: Yeah , it was <disfmarker> it was slightly worse .
speaker: B
text: Uh .
speaker: D
text: Slightly bad .
speaker: D
text: Yeah .
speaker: C
text: Um ,
speaker: B
text: Yeah , it should be pretty close to cancelled out .
speaker: D
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: A
text: You know , I 've been wondering about something .
speaker: A
text: In the , um <disfmarker> a lot of the , um <disfmarker> the Hub - five systems , um , recently have been using LDA .
speaker: A
text: and <disfmarker> and they , um <disfmarker> They run LDA on the features right before they train the models .
speaker: A
text: So there 's the <disfmarker> the LDA is <disfmarker> is right there before the H M
speaker: D
text: Yeah .
speaker: A
text: So , you guys are using LDA
speaker: A
text: but it seems like it 's pretty far back in the process .
speaker: D
text: Uh , this LDA is different from the LDA that you are talking about .
speaker: D
text: The LDA that you <disfmarker> saying is , like , you take a block of features , like nine frames or something , <comment> and then do an LDA on it ,
speaker: A
text: Yeah .
speaker: A
text: Uh - huh .
speaker: D
text: and then reduce the dimensionality to something like twenty - four or something like that .
speaker: A
text: Yeah , you c you c you can .
speaker: D
text: And then feed it to HMM .
speaker: A
text: I mean , it 's <disfmarker> you know , you 're just basically i
speaker: D
text: Yeah , so this is like a two d two dimensional tile .
speaker: A
text: You 're shifting the feature space .
speaker: A
text: Yeah .
speaker: D
text: So this is a two dimensional tile .
speaker: D
text: And the LDA that we are f applying is only in time ,
speaker: D
text: not in frequency <disfmarker>
speaker: D
text: high cost frequency .
speaker: D
text: So it 's like <disfmarker> more like a filtering in time ,
speaker: D
text: rather than doing a r
speaker: A
text: Ah . OK .
speaker: A
text: So what i what about , um <disfmarker> i u what i w
speaker: A
text: I mean , I don't know if this is a good idea or not ,
speaker: A
text: but what if you put <disfmarker> ran the other kind of LDA , uh , on your features right before they go into the HMM ?
speaker: D
text: Uh , it <disfmarker>
speaker: C
text: Mm - hmm .
speaker: C
text: No , actually , I think <disfmarker> i
speaker: D
text: m
speaker: C
text: Well .
speaker: C
text: What do we do with the ANN is <disfmarker> is something like that
speaker: C
text: except that it 's not linear .
speaker: C
text: But it 's <disfmarker> it 's like a nonlinear discriminant analysis .
speaker: A
text: Yeah .
speaker: A
text: Right ,
speaker: A
text: it 's the <disfmarker> It 's <disfmarker>
speaker: A
text: Right .
speaker: A
text: The <disfmarker> So <disfmarker>
speaker: C
text: But .
speaker: A
text: Yeah , so it 's sort of like <disfmarker>
speaker: A
text: The tandem stuff is kind of like i nonlinear LDA .
speaker: C
text: Yeah .
speaker: C
text: It 's <disfmarker>
speaker: A
text: I g
speaker: C
text: Yeah .
speaker: B
text: Yeah .
speaker: A
text: Yeah .
speaker: C
text: Uh .
speaker: A
text: But I mean , w but the other features that you have , um , th the non - tandem ones ,
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah , I know .
speaker: C
text: That <disfmarker> that <disfmarker> Yeah .
speaker: C
text: Well , in the proposal , they were transformed u using PCA ,
speaker: C
text: but <disfmarker>
speaker: A
text: Uh - huh .
speaker: C
text: Yeah , it might be that LDA could be better .
speaker: B
text: The a the argument i is kind of i in <disfmarker>
speaker: B
text: and it 's not like we really know ,
speaker: B
text: but the argument anyway is that , um , uh , we always have the prob
speaker: B
text: I mean , discriminative things are good .
speaker: B
text: LDA , neural nets , they 're good .
speaker: A
text: Yeah .
speaker: B
text: Uh , they 're good because you <disfmarker> you <disfmarker> you learn to distinguish between these categories that you want to be good at distinguishing between .
speaker: B
text: And PCA doesn't do that .
speaker: B
text: It <disfmarker> PAC - PCA <disfmarker> low - order PCA throws away pieces that are uh , maybe not <disfmarker> not gonna be helpful just because they 're small , basically .
speaker: A
text: Right .
speaker: B
text: But , uh , the problem is , training sets aren't perfect and testing sets are different .
speaker: B
text: So you f you <disfmarker> you face the potential problem with discriminative stuff , be it LDA or neural nets , that you are training to discriminate between categories in one space
speaker: B
text: but what you 're really gonna be g getting is <disfmarker> is something else .
speaker: A
text: Uh - huh .
speaker: B
text: And so , uh , Stephane 's idea was , uh , let 's feed , uh , both this discriminatively trained thing and something that 's not .
speaker: B
text: So you have a good set of features that everybody 's worked really hard to make ,
speaker: A
text: Yeah .
speaker: B
text: and then , uh , you <disfmarker> you discriminately train it ,
speaker: B
text: but you also take the path that <disfmarker> that doesn't have that ,
speaker: A
text: Uh - huh .
speaker: B
text: and putting those in together .
speaker: B
text: And that <disfmarker> that seem
speaker: B
text: So it 's kind of like a combination of the <disfmarker> uh , what , uh , Dan has been calling , you know , a feature <disfmarker> uh , you know , a feature combination versus posterior combination or something .
speaker: B
text: It 's <disfmarker> it 's , you know , you have the posterior combination
speaker: B
text: but then you get the features from that and use them as a feature combination with these <disfmarker> these other things .
speaker: B
text: And that seemed , at least in the last one , as he was just saying , he <disfmarker> he <disfmarker> when he only did discriminative stuff , i it actually was <disfmarker> was <disfmarker> it didn't help at all in this particular case .
speaker: A
text: Yeah .
speaker: B
text: There was enough of a difference , I guess , between the testing and training .
speaker: B
text: But by having them both there <disfmarker>
speaker: B
text: The fact is some of the time , the discriminative stuff is gonna help you .
speaker: A
text: Mm - hmm .
speaker: B
text: And some of the time it 's going to hurt you ,
speaker: B
text: and by combining two information sources if , you know <disfmarker> if <disfmarker> if <disfmarker>
speaker: A
text: Right .
speaker: A
text: So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that <disfmarker>
speaker: B
text: That i i
speaker: B
text: I think that 's counter to that idea .
speaker: A
text: Yeah ,
speaker: B
text: Now , again , it 's <disfmarker> we 're just trying these different things .
speaker: A
text: right .
speaker: B
text: We don't really know what 's gonna work best .
speaker: B
text: But if that 's the hypothesis , at least it would be counter to that hypothesis to do that .
speaker: A
text: Right .
speaker: B
text: Um ,
speaker: B
text: and in principle you would think that the neural net would do better at the discriminant part than LDA .
speaker: A
text: Right .
speaker: A
text: Yeah .
speaker: B
text: Though , maybe not .
speaker: A
text: Well <disfmarker> y
speaker: A
text: Yeah .
speaker: A
text: Exactly .
speaker: A
text: I mean , we , uh <disfmarker> we were getting ready to do the tandem , uh , stuff for the Hub - five system ,
speaker: A
text: and , um , Andreas and I talked about it ,
speaker: A
text: and the idea w the thought was , " Well , uh , yeah , that i you know <disfmarker> th the neural net should be better ,
speaker: A
text: but we should at least have uh , a number , you know , to show that we did try the LDA in place of the neural net ,
speaker: A
text: so that we can you know , show a clear path .
speaker: B
text: Right .
speaker: A
text: You know , that you have it without it ,
speaker: A
text: then you have the LDA ,
speaker: A
text: then you have the neural net ,
speaker: A
text: and you can see , theoretically .
speaker: A
text: So .
speaker: A
text: I was just wondering <disfmarker>
speaker: A
text: I <disfmarker> I <disfmarker>
speaker: B
text: Well , I think that 's a good idea .
speaker: A
text: Yeah .
speaker: B
text: Did <disfmarker> did you do that
speaker: B
text: or <disfmarker> tha that 's a <disfmarker>
speaker: A
text: Um . No .
speaker: A
text: That 's what <disfmarker> that 's what we 're gonna do next
speaker: A
text: as soon as I finish this other thing .
speaker: B
text: Yeah .
speaker: A
text: So .
speaker: B
text: Yeah .
speaker: B
text: No , well , that 's a good idea .
speaker: B
text: I <disfmarker> I <disfmarker>
speaker: B
text: i Yeah .
speaker: A
text: We just want to show .
speaker: A
text: I mean , it <disfmarker> everybody believes it ,
speaker: B
text: Oh , no
speaker: B
text: it 's a g
speaker: A
text: but you know , we just <disfmarker>
speaker: B
text: No , no ,
speaker: B
text: but it might not <disfmarker> not even be true .
speaker: B
text: I mean , it 's <disfmarker> it 's <disfmarker> it 's <disfmarker> it 's <disfmarker> it 's a great idea .
speaker: A
text: Yeah .
speaker: B
text: I mean , one of the things that always disturbed me , uh , in the <disfmarker> the resurgence of neural nets that happened in the eighties was that , um , a lot of people <disfmarker> Because neural nets were pretty easy to <disfmarker> to use <disfmarker> a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh <disfmarker> uh , versions of them .
speaker: A
text: Yeah .
speaker: A
text: Mm - hmm .
speaker: B
text: And , uh , people were doing recurrent nets but not looking at IIR filters ,
speaker: A
text: Yeah .
speaker: B
text: and <disfmarker> You know , I mean , uh ,
speaker: B
text: so I think , yeah , it 's definitely a good idea to try it .
speaker: A
text: Yeah ,
speaker: A
text: and everybody 's putting that on their <vocalsound> systems now ,
speaker: A
text: and so , I
speaker: A
text: that 's what made me wonder about this ,
speaker: B
text: Well , they 've been putting them in their systems off and on for ten years ,
speaker: A
text: but .
speaker: B
text: but <disfmarker> but <disfmarker> but , uh ,
speaker: A
text: Yeah , what I mean is it 's <disfmarker> it 's like in the Hub - five evaluations , you know ,
speaker: A
text: and you read the system descriptions and everybody 's got , <vocalsound> you know , LDA on their features .
speaker: B
text: And now they all have that .
speaker: B
text: I see .
speaker: A
text: And so .
speaker: B
text: Yeah .
speaker: A
text: Uh .
speaker: C
text: It 's the transformation they 're estimating on <disfmarker>
speaker: C
text: Well , they are trained on the same data as the final HMM are .
speaker: A
text: Yeah , so it 's different .
speaker: A
text: Yeah ,
speaker: A
text: exactly .
speaker: A
text: Cuz they don't have these , you know , mismatches that <disfmarker> that you guys have .
speaker: C
text: Mm - hmm .
speaker: A
text: So that 's why I was wondering if maybe it 's not even a good idea .
speaker: C
text: Mm - hmm .
speaker: A
text: I don't know .
speaker: A
text: I <disfmarker> I don't know enough about it ,
speaker: C
text: Mm - hmm .
speaker: A
text: but <disfmarker> Um .
speaker: B
text: I mean , part of why <disfmarker>
speaker: B
text: I <disfmarker> I think part of why you were getting into the KLT <disfmarker> Y you were describing to me at one point that you wanted to see if , uh , you know , getting good orthogonal features was <disfmarker> and combining the <disfmarker> the different temporal ranges <disfmarker> was the key thing that was happening or whether it was this discriminant thing , right ?
speaker: B
text: So you were just trying <disfmarker>
speaker: B
text: I think you r
speaker: B
text: I mean , this is <disfmarker> it doesn't have the LDA aspect
speaker: B
text: but th as far as the orthogonalizing transformation , you were trying that at one point , right ?
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: I think you were .
speaker: C
text: Yeah .
speaker: B
text: Does something .
speaker: B
text: It doesn't work as well .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: D
text: So , yeah ,
speaker: D
text: I 've been exploring a parallel VAD without neural network
speaker: D
text: with , like , less latency using SNR and energy , um , after the cleaning up .
speaker: D
text: So what I 'd been trying was , um ,
speaker: D
text: uh <disfmarker>
speaker: D
text: After the b after the noise compensation , n I was trying t to f find a f feature based on the ratio of the energies , that is , cl after clean and before clean .
speaker: D
text: So that if <disfmarker> if they are , like , pretty c close to one , which means it 's speech .
speaker: D
text: And if it is n if it is close to zero , which is <disfmarker> So it 's like a scale @ @ probability value .
speaker: D
text: So I was trying , uh , with full band and multiple bands ,
speaker: D
text: m ps uh <disfmarker> separating them to different frequency bands
speaker: D
text: and deriving separate decisions on each bands , and trying to combine them .
speaker: D
text: Uh ,
speaker: D
text: the advantage being like it doesn't have the latency of the neural net if it <disfmarker> if it can
speaker: B
text: Mm - hmm .
speaker: D
text: g And <pause> it gave me like , uh , one point <disfmarker> One <disfmarker> more than one percent relative improvement .
speaker: D
text: So , from fifty - three point six it went to fifty f four point eight .
speaker: D
text: So it 's , like , only slightly more than a percent improvement ,
speaker: B
text: Mm - hmm .
speaker: D
text: just like <disfmarker>
speaker: D
text: Which means that it 's <disfmarker> it 's doing a slightly better job than the previous VAD ,
speaker: B
text: Mm - hmm .
speaker: D
text: uh , at a l lower delay .
speaker: B
text: Mm - hmm .
speaker: D
text: Um ,
speaker: D
text: so , um <disfmarker>
speaker: D
text: so <disfmarker> u
speaker: B
text: But <disfmarker>
speaker: B
text: i d I 'm sorry ,
speaker: B
text: does it still have the median <pause> filter stuff ?
speaker: D
text: It still has the median filter .
speaker: B
text: So it still has most of the delay ,
speaker: D
text: So <disfmarker>
speaker: B
text: it just doesn't <disfmarker>
speaker: D
text: Yeah ,
speaker: D
text: so d with the delay , that 's gone is the input , which is the sixty millisecond .
speaker: D
text: The forty plus <pause> twenty .
speaker: D
text: At the input of the neural net you have this , uh , f nine frames of context plus the delta .
speaker: B
text: Well , w i
speaker: C
text: Mm - hmm .
speaker: B
text: Oh , plus the delta ,
speaker: B
text: right .
speaker: D
text: Yeah .
speaker: B
text: OK .
speaker: D
text: So that delay , plus the LDA .
speaker: B
text: Mm - hmm .
speaker: D
text: Uh , so the delay is only the forty millisecond of the noise cleaning , plus the hundred millisecond smoothing at the output .
speaker: B
text: Mm - hmm .
speaker: B
text: Mm - hmm .
speaker: D
text: Um .
speaker: D
text: So . Yeah .
speaker: D
text: So the <disfmarker> the <disfmarker> di the biggest <disfmarker>
speaker: D
text: The problem f for me was to find a consistent threshold that works <pause> well across the different databases ,
speaker: D
text: because I t I try to make it work on tr SpeechDat - Car
speaker: B
text: Mm - hmm .
speaker: D
text: and it fails on TI - digits ,
speaker: D
text: or if I try to make it work on that it 's just the Italian or something , it doesn't work on the Finnish .
speaker: B
text: Mm - hmm .
speaker: D
text: So ,
speaker: D
text: um .
speaker: D
text: So there are <disfmarker> there was , like , some problem in balancing the deletions and insertions when I try different thresholds .
speaker: B
text: Mm - hmm .
speaker: D
text: So <disfmarker>
speaker: D
text: The <disfmarker>
speaker: D
text: I 'm still trying to make it better by using some other features from the <disfmarker> after the p clean up <disfmarker>
speaker: D
text: maybe , some , uh , correlation <disfmarker> auto - correlation or some s additional features of <disfmarker> to mainly the improvement of the VAD . I 've been trying .
speaker: B
text: Now this <disfmarker> this <disfmarker> this , uh , " before and after clean " , it sounds like you think that 's a good feature .
speaker: B
text: That <disfmarker> that , it <disfmarker> you th think that the , uh <disfmarker> the <disfmarker> i it appears to be a good feature , right ?
speaker: D
text: Mm - hmm .
speaker: D
text: Yeah .
speaker: B
text: What about using it in the neural net ?
speaker: C
text: Yeah ,
speaker: C
text: eventually we could <disfmarker> could just
speaker: D
text: Yeah ,
speaker: D
text: so <disfmarker> Yeah ,
speaker: D
text: so that 's the <disfmarker>
speaker: D
text: Yeah .
speaker: D
text: So we 've been thinking about putting it into the neural net also .
speaker: B
text: Yeah .
speaker: D
text: Because they did <disfmarker> that itself <disfmarker>
speaker: C
text: Then you don't have to worry about the thresholds and <disfmarker>
speaker: D
text: There 's a threshold
speaker: D
text: and <disfmarker> Yeah .
speaker: D
text: Yeah .
speaker: B
text: Yeah .
speaker: D
text: So that <disfmarker> that 's , uh <disfmarker>
speaker: C
text: but just <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: So if we <disfmarker> if we can live with the latency or cut the latencies elsewhere , then <disfmarker> then that would be a , uh , good thing .
speaker: D
text: Yeah .
speaker: D
text: Yeah .
speaker: B
text: Um , anybody <disfmarker> has anybody <disfmarker> you guys or <disfmarker> or Naren , uh , somebody , tried the , uh , um , second th second stream thing ?
speaker: D
text: Oh , I just <disfmarker> I just h put the second stream in place and , uh ran one experiment ,
speaker: B
text: Uh .
speaker: D
text: but just like <disfmarker> just to know that everything is fine .
speaker: B
text: Uh - huh .
speaker: D
text: So it was like , uh , forty - five cepstrum plus twenty - three mel <disfmarker> log mel .
speaker: B
text: Yeah .
speaker: D
text: And <disfmarker> and , just , like , it gave me the baseline performance of the Aurora ,
speaker: D
text: which is like zero improvement .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: D
text: So I just tried it on Italian just to know that everything is <disfmarker>
speaker: D
text: But I <disfmarker> I didn't export anything out of it
speaker: D
text: because it was , like , a weird feature set .
speaker: B
text: Yeah .
speaker: D
text: So .
speaker: B
text: Yeah .
speaker: B
text: Well , what I think , you know , would be more what you 'd want to do is <disfmarker> is <disfmarker> is , uh , put it into another neural net .
speaker: D
text: Yeah , yeah , yeah , yeah .
speaker: C
text: Mm - hmm .
speaker: B
text: Right ?
speaker: B
text: And then <disfmarker>
speaker: B
text: But ,
speaker: B
text: yeah , we 're <disfmarker> we 're not quite there yet .
speaker: B
text: So we have to <vocalsound> figure out the neural nets , I guess .
speaker: C
text: Yeah .
speaker: D
text: The uh , other thing I was wondering was , um , if the neural net , um , has any <disfmarker> because of the different noise con unseen noise conditions for the neural net ,
speaker: D
text: where , like , you train it on those four noise conditions , while you are feeding it with , like , a additional <disfmarker> some four plus some <disfmarker> f few more conditions which it hasn't seen , actually ,
speaker: C
text: Mm - hmm .
speaker: D
text: from the <disfmarker> f f while testing .
speaker: C
text: Yeah , yeah .
speaker: C
text: Right .
speaker: D
text: Um <disfmarker>
speaker: D
text: instead of just h having c uh , those cleaned up t cepstrum , sh should we feed some additional information , like <disfmarker> The <disfmarker> the <disfmarker>
speaker: D
text: We have the VAD flag .
speaker: D
text: I mean , should we f feed the VAD flag , also , at the input so that it <disfmarker> it has some additional discriminating information at the input ?
speaker: C
text: Hmm - hmm !
speaker: C
text: Um <disfmarker>
speaker: B
text: Wh - uh , the <disfmarker> the VAD what ?
speaker: D
text: We have the VAD information also available at the back - end .
speaker: B
text: Uh - huh .
speaker: D
text: So if it is something the neural net is not able to discriminate the classes <disfmarker>
speaker: B
text: Yeah .
speaker: D
text: I mean <disfmarker>
speaker: D
text: Because most of it is sil
speaker: D
text: I mean , we have dropped some silence f We have dropped so silence frames ?
speaker: B
text: Mm - hmm .
speaker: D
text: No , we haven't dropped silence frames still .
speaker: C
text: Uh , still not .
speaker: C
text: Yeah .
speaker: D
text: Yeah .
speaker: D
text: So <disfmarker>
speaker: D
text: the b b biggest classification would be the speech and silence .
speaker: C
text: Th
speaker: D
text: So , by having an additional , uh , feature which says " this is speech and this is nonspeech " , I mean , it certainly helps in some unseen noise conditions for the neural net .
speaker: A
text: What <disfmarker>
speaker: A
text: Do y do you have that feature available for the test data ?
speaker: D
text: Well , I mean , we have <disfmarker> we are transferring the VAD to the back - end <disfmarker>
speaker: D
text: feature to the back - end .
speaker: D
text: Because we are dropping it at the back - end after everything <disfmarker> all the features are computed .
speaker: A
text: Oh , oh , I see .
speaker: D
text: So <disfmarker>
speaker: A
text: I see .
speaker: D
text: so the neural <disfmarker>
speaker: D
text: so that is coming from a separate neural net or some VAD .
speaker: A
text: OK .
speaker: A
text: OK .
speaker: D
text: Which is <disfmarker> which is certainly giving a
speaker: A
text: So you 're saying , feed that , also , into <pause> the neural net .
speaker: D
text: to <disfmarker>
speaker: D
text: Yeah . So it it 's an <disfmarker> additional discriminating information .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: Right .
speaker: D
text: So that <disfmarker>
speaker: B
text: You could feed it into the neural net .
speaker: B
text: The other thing <comment> you could do is just , um , p modify the , uh , output probabilities of the <disfmarker> of the , uh , uh , um , neural net , tandem neural net , <comment> based on the fact that you have a silence probability .
speaker: D
text: Mm - hmm .
speaker: B
text: Right ?
speaker: C
text: Mm - hmm .
speaker: B
text: So you have an independent estimator of what the silence probability is ,
speaker: B
text: and you could multiply the two things , and renormalize .
speaker: B
text: Uh , I mean , you 'd have to do the nonlinearity part and deal with that .
speaker: C
text: Yeah .
speaker: B
text: Uh , I mean , go backwards from what the nonlinearity would , you know <disfmarker> would be .
speaker: D
text: Through <disfmarker> t to the soft max .
speaker: B
text: But <disfmarker> but , uh <disfmarker>
speaker: C
text: Yeah , so <disfmarker>
speaker: C
text: maybe , yeah , when <disfmarker>
speaker: A
text: But in principle wouldn't it be better to feed it in ?
speaker: A
text: And let the net do that ?
speaker: B
text: Well , u Not sure .
speaker: B
text: I mean ,
speaker: A
text: Hmm .
speaker: B
text: let 's put it this way .
speaker: B
text: I mean , y you <disfmarker> you have this complicated system with thousands and thousand parameters
speaker: A
text: Yeah .
speaker: B
text: and you can tell it , uh , " Learn this thing . "
speaker: B
text: Or you can say , " It 's silence !
speaker: B
text: Go away ! "
speaker: B
text: I mean ,
speaker: B
text: I mean , i
speaker: B
text: Doesn't <disfmarker> ?
speaker: B
text: I think <disfmarker> I think the second one sounds a lot more direct .
speaker: A
text: What <disfmarker>
speaker: A
text: what if you <disfmarker>
speaker: B
text: Uh .
speaker: A
text: Right .
speaker: A
text: So , what if you then , uh <disfmarker> since you know this , what if you only use the neural net on the speech portions ?
speaker: B
text: Well , uh ,
speaker: C
text: That 's what <disfmarker>
speaker: A
text: Well , I guess that 's the same .
speaker: A
text: Uh , that 's similar .
speaker: B
text: Yeah ,
speaker: B
text: I mean , y you 'd have to actually run it continuously ,
speaker: B
text: but it 's <disfmarker> @ @ <disfmarker>
speaker: A
text: But I mean <disfmarker> I mean , train the net only on <disfmarker>
speaker: B
text: Well , no ,
speaker: B
text: you want to train on <disfmarker> on the nonspeech also ,
speaker: B
text: because that 's part of what you 're learning in it ,
speaker: B
text: to <disfmarker> to <disfmarker> to generate , that it 's <disfmarker> it has to distinguish between .
speaker: D
text: Speech .
speaker: A
text: But I mean , if you 're gonna <disfmarker> if you 're going to multiply the output of the net by this other decision , uh , would <disfmarker> then you don't care about whether the net makes that distinction , right ?
speaker: B
text: Well , yeah .
speaker: B
text: But this other thing isn't perfect .
speaker: A
text: Ah .
speaker: B
text: So that you bring in some information from the net itself .
speaker: A
text: Right ,
speaker: A
text: OK .
speaker: A
text: That 's a good point .
speaker: B
text: Yeah .
speaker: B
text: Now the only thing that <disfmarker> that bothers me about all this is that I <disfmarker> I <disfmarker> I <disfmarker> The <disfmarker> the fact <disfmarker>
speaker: B
text: i i It 's sort of bothersome that you 're getting more deletions .
speaker: C
text: Yeah .
speaker: C
text: But <disfmarker>
speaker: C
text: So I might maybe look at ,
speaker: C
text: is it due to the fact that um , the probability of the silence at the output of the network , is ,
speaker: C
text: uh ,
speaker: B
text: Is too high .
speaker: C
text: too <disfmarker> too high
speaker: C
text: or <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: So maybe <disfmarker>
speaker: C
text: If it 's the case , then multiplying it again by <disfmarker> i by something ?
speaker: B
text: So <disfmarker>
speaker: D
text: It may not be <disfmarker> it <disfmarker>
speaker: B
text: Yeah .
speaker: D
text: Yeah ,
speaker: C
text: Mm - hmm .
speaker: D
text: it <disfmarker> it may be too <disfmarker> it 's too high in a sense , like , everything is more like a , um , flat probability .
speaker: B
text: Yeah .
speaker: C
text: Oh - eee - hhh .
speaker: D
text: So , like , it 's not really doing any distinction between speech and nonspeech <disfmarker>
speaker: C
text: Uh , yeah .
speaker: D
text: or , I mean , different <disfmarker> among classes .
speaker: B
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: A
text: Be interesting to look at the <disfmarker>
speaker: A
text: Yeah ,
speaker: A
text: for the <disfmarker>
speaker: A
text: I wonder if you could do this .
speaker: A
text: But if you look at the , um , highly mism high mismat the output of the net on the high mismatch case and just look at , you know , the distribution versus the <disfmarker> the other ones , do you <disfmarker> do you see more peaks or something ?
speaker: C
text: Yeah .
speaker: C
text: Yeah , like the entropy of the <disfmarker> the output ,
speaker: A
text: Yeah .
speaker: C
text: or <disfmarker>
speaker: B
text: Yeah , for instance .
speaker: B
text: But I <disfmarker> bu
speaker: C
text: It <disfmarker> it seems that the VAD network doesn't <disfmarker> Well , it doesn't drop , uh , too many frames
speaker: C
text: because the dele the number of deletion is reasonable .
speaker: C
text: But it 's just when we add the tandem , the final MLP , and then <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: Now the only problem is you don't want to ta I guess wait for the output of the VAD before you can put something into the other system ,
speaker: C
text: u
speaker: B
text: cuz that 'll shoot up the latency a lot ,
speaker: B
text: right ?
speaker: B
text: Am I missing something here ?
speaker: C
text: But <disfmarker>
speaker: D
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: C
text: Right .
speaker: B
text: Yeah .
speaker: B
text: So that 's maybe a problem with what I was just saying .
speaker: B
text: But <disfmarker>
speaker: B
text: but <disfmarker> I I guess <disfmarker>
speaker: A
text: But if you were gonna put it in as a feature it means you already have it by the time you get to the tandem net ,
speaker: A
text: right ?
speaker: D
text: Um ,
speaker: D
text: well . We <disfmarker> w we don't have it , actually ,
speaker: D
text: because it 's <disfmarker> it has a high rate energy <disfmarker>
speaker: B
text: No .
speaker: D
text: the VAD has a <disfmarker>
speaker: A
text: Ah .
speaker: B
text: Yeah .
speaker: A
text: OK .
speaker: B
text: It 's kind of done in <disfmarker>
speaker: B
text: I mean , some of the things are , not in parallel ,
speaker: B
text: but certainly , it would be in parallel with the <disfmarker> with a tandem net .
speaker: A
text: Right .
speaker: B
text: In time .
speaker: B
text: So maybe , if that doesn't work ,
speaker: B
text: um <disfmarker>
speaker: B
text: But it would be interesting to see if that was the problem , anyway .
speaker: B
text: And <disfmarker> and <disfmarker> and then I guess another alternative would be to take the feature that you 're feeding into the VAD , and feeding it into the other one as well .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: And then maybe it would just learn <disfmarker> learn it better .
speaker: B
text: Um <disfmarker>
speaker: B
text: But that 's <disfmarker>
speaker: B
text: Yeah , that 's an interesting thing to try to see , if what 's going on is that in the highly mismatched condition , it 's , um , causing deletions by having this silence probability up <disfmarker> up too high ,
speaker: C
text: Mm - hmm .
speaker: B
text: at some point where the VAD is saying it 's actually speech .
speaker: C
text: Yeah .
speaker: C
text: So , m
speaker: B
text: Which is probably true .
speaker: B
text: Cuz <disfmarker>
speaker: B
text: Well , the V A
speaker: B
text: if the VAD said <disfmarker>
speaker: B
text: since the VAD is <disfmarker> is <disfmarker> is right a lot ,
speaker: B
text: uh <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: Hmm .
speaker: B
text: Anyway .
speaker: B
text: Might be .
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: B
text: Well , we just started working with it .
speaker: B
text: But these are <disfmarker> these are some good ideas I think .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah , and the other thing <disfmarker>
speaker: C
text: Well , there are other issues maybe for the tandem ,
speaker: C
text: like , uh , well , do we want to , w uh n Do we want to work on the targets ?
speaker: C
text: Or , like , instead of using phonemes , using more context dependent units ?
speaker: A
text: For the tandem net you mean ?
speaker: C
text: Well , I 'm <disfmarker>
speaker: C
text: Yeah .
speaker: A
text: Hmm .
speaker: C
text: I 'm thinking , also , a w about Dan 's work where he <disfmarker> he trained <vocalsound> a network , not on phoneme targets but on the HMM state targets .
speaker: C
text: And <disfmarker>
speaker: C
text: it was giving s slightly better results .
speaker: B
text: Problem is , if you are going to run this on different m test sets , including large vocabulary ,
speaker: C
text: Yeah .
speaker: C
text: Yeah .
speaker: B
text: um ,
speaker: C
text: Uh <disfmarker>
speaker: C
text: Mmm .
speaker: B
text: I think <disfmarker>
speaker: C
text: I was just thinking maybe about , like , generalized diphones ,
speaker: C
text: and <disfmarker> come up with a <disfmarker> a reasonable , not too large , set of context dependent units ,
speaker: C
text: and <disfmarker>
speaker: C
text: and <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: And then anyway we would have to reduce this with the KLT .
speaker: B
text: Yeah .
speaker: C
text: So .
speaker: C
text: But <disfmarker>
speaker: C
text: I don't know .
speaker: B
text: Yeah .
speaker: B
text: Well , maybe .
speaker: C
text: Mm - hmm .
speaker: B
text: But I d I d it <disfmarker> it <disfmarker> i it 's all worth looking at ,
speaker: B
text: but it sounds to me like , uh , looking at the relationship between this and the <disfmarker> speech noise stuff is <disfmarker> is <disfmarker> is probably a key thing .
speaker: C
text: Mm - hmm .
speaker: B
text: That and the correlation between stuff .
speaker: A
text: So if ,
speaker: A
text: uh <disfmarker>
speaker: A
text: if the , uh , high mismatch case had been more like the , uh , the other two cases <comment> in terms of giving you just a better performance , <comment> how would this number have changed ?
speaker: C
text: Mm - hmm .
speaker: C
text: Oh , it would be <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: Around five percent better , I guess .
speaker: C
text: If <disfmarker> if <disfmarker> i
speaker: A
text: y Like sixty ?
speaker: B
text: Well , we don't know what 's it 's gonna be the TI - digits yet .
speaker: B
text: He hasn't got the results back yet .
speaker: C
text: Yeah .
speaker: C
text: If you extrapolate the SpeechDat - Car well - matched and medium - mismatch , it 's around , yeah , maybe five .
speaker: A
text: Uh - huh .
speaker: A
text: Yeah .
speaker: A
text: So this would be sixty - two ?
speaker: B
text: Sixty - two .
speaker: C
text: Sixty - two ,
speaker: B
text: Yeah .
speaker: D
text: Somewhere around sixty , must be .
speaker: A
text: Which is <disfmarker>
speaker: C
text: yeah .
speaker: D
text: Right ?
speaker: D
text: Yeah .
speaker: C
text: Well , it 's around five percent , because it 's <disfmarker> s
speaker: D
text: Yeah .
speaker: C
text: Right ?
speaker: C
text: If everything is five percent .
speaker: D
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: A
text: All the other ones were five percent ,
speaker: A
text: the <disfmarker>
speaker: C
text: I d I d I just have the SpeechDat - Car right now ,
speaker: B
text: Yeah .
speaker: C
text: so <disfmarker>
speaker: A
text: Yeah .
speaker: C
text: It 's running <disfmarker>
speaker: C
text: it shou we should have the results today during the afternoon ,
speaker: A
text: Hmm .
speaker: C
text: but <disfmarker>
speaker: C
text: Well .
speaker: B
text: Hmm .
speaker: B
text: Well <disfmarker>
speaker: B
text: Um <disfmarker>
speaker: B
text: So I won't be here for <disfmarker>
speaker: A
text: When <disfmarker>
speaker: A
text: When do you leave ?
speaker: B
text: Uh , I 'm leaving next Wednesday .
speaker: B
text: May or may not be in in the morning .
speaker: B
text: I leave in the afternoon .
speaker: B
text: Um ,
speaker: B
text: so I <disfmarker>
speaker: A
text: But you 're <disfmarker>
speaker: A
text: are you <disfmarker>
speaker: A
text: you 're not gonna be around this afternoon ?
speaker: B
text: Yeah .
speaker: A
text: Oh .
speaker: B
text: Oh , well .
speaker: B
text: I 'm talking about next week .
speaker: B
text: I 'm leaving <disfmarker> leaving next Wednesday .
speaker: A
text: Uh - huh .
speaker: B
text: This afternoon <disfmarker> uh <disfmarker>
speaker: B
text: Oh , right ,
speaker: B
text: for the Meeting meeting ?
speaker: B
text: Yeah , that 's just cuz of something on campus .
speaker: A
text: Ah ,
speaker: A
text: OK ,
speaker: A
text: OK .
speaker: B
text: Yeah .
speaker: B
text: But , um ,
speaker: B
text: yeah ,
speaker: B
text: so next week I won't ,
speaker: B
text: and the week after I won't ,
speaker: B
text: cuz I 'll be in Finland .
speaker: B
text: And the week after that I won't .
speaker: B
text: By that time you 'll be <disfmarker> <comment> Uh , you 'll both be gone <pause> from here .
speaker: B
text: So there 'll be no <disfmarker> definitely no meeting on <disfmarker> on September sixth .
speaker: B
text: Uh ,
speaker: B
text: and <disfmarker>
speaker: A
text: What 's September sixth ?
speaker: B
text: Uh , that 's during Eurospeech .
speaker: A
text: Oh , oh , right .
speaker: A
text: OK .
speaker: B
text: So , uh , Sunil will be in Oregon .
speaker: B
text: Uh , Stephane and I will be in Denmark .
speaker: B
text: Uh <disfmarker>
speaker: B
text: Right ?
speaker: B
text: So it 'll be a few weeks , really , before we have a meeting of the same cast of characters .
speaker: B
text: Um ,
speaker: B
text: but , uh <disfmarker>
speaker: B
text: I guess , just <disfmarker>
speaker: B
text: I mean , you guys should probably meet .
speaker: B
text: And maybe Barry <disfmarker> Barry will be around .
speaker: B
text: And <disfmarker>
speaker: B
text: and then uh , uh , we 'll start up again with Dave and <disfmarker> Dave and Barry and Stephane and us on the , uh , twentieth .
speaker: B
text: No .
speaker: B
text: Thirteenth ?
speaker: B
text: About a month ?
speaker: A
text: So ,
speaker: A
text: uh , you 're gonna be gone for the next three weeks or something ?
speaker: B
text: I 'm gone for two and a half weeks starting <disfmarker> starting next Wed - late next Wednesday .
speaker: A
text: So that 's <disfmarker> you won't be at the next three of these meetings .
speaker: A
text: Is that right ?
speaker: B
text: Uh , I won't <disfmarker>
speaker: B
text: it 's probably four because of <disfmarker>
speaker: B
text: is it three ?
speaker: B
text: Let 's see ,
speaker: B
text: twenty - third ,
speaker: B
text: thirtieth ,
speaker: B
text: sixth .
speaker: B
text: That 's right ,
speaker: B
text: next three .
speaker: B
text: And the <disfmarker> the third one won't <disfmarker> probably won't be a meeting ,
speaker: B
text: cuz <disfmarker> cuz , uh , Su - Sunil , Stephane , and I will all not be here .
speaker: A
text: Oh , right .
speaker: A
text: Right .
speaker: B
text: Um <disfmarker>
speaker: B
text: Mmm . <comment> So it 's just , uh , the next two where there will be <disfmarker> there , you know , may as well be meetings ,
speaker: B
text: but I just won't be at them .
speaker: A
text: OK .
speaker: B
text: And then starting up on the thirteenth , <nonvocalsound> uh , we 'll have meetings again
speaker: B
text: but we 'll have to do without Sunil here somehow .
speaker: A
text: When do you go back ?
speaker: B
text: So .
speaker: D
text: Thirty - first , August .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: So .
speaker: B
text: Cool .
speaker: A
text: When is the evaluation ?
speaker: A
text: November ,
speaker: A
text: or something ?
speaker: B
text: Yeah ,
speaker: B
text: it was supposed to be November fifteenth .
speaker: B
text: Has anybody heard anything different ?
speaker: C
text: I don't know .
speaker: C
text: The meeting in <disfmarker> is the five and six of December .
speaker: D
text: p s It 's like <disfmarker> Yeah , it 's tentatively all full .
speaker: C
text: So <disfmarker>
speaker: D
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: D
text: Uh , that 's a proposed date , I guess .
speaker: C
text: Yeah ,
speaker: C
text: um <disfmarker>
speaker: C
text: so the evaluation should be on a week before
speaker: C
text: or <disfmarker>
speaker: A
text: Yeah .
speaker: B
text: Yep .
speaker: B
text: But , no , this is good progress .
speaker: B
text: So .
speaker: B
text: Uh <disfmarker>
speaker: B
text: OK .
speaker: B
text: Guess we 're done .
speaker: A
text: Should we do digits ?
speaker: B
text: Digits ?
speaker: B
text: Yep .
speaker: A
text: OK .
speaker: B
text: It 's a wrap .

Meeting summary:
text: ICSI's Meeting Recorder Group at Berkeley meets to discuss, for the most part, progress on the Aurora Project.
text: The main areas being worked on were the voice activity detector and the tandem data streams.
text: The group discussed possible further investigations that arose from these areas, including better linking the two.
text: They also consider how aspects of an absent member's work might be applied to the current project.
text: The meeting closed with a discussion of upcoming absences, and how meetings would continue.
text: Speaker me018 must confirm what is needed to work with the new software in terms of adjustments with someone further up the project chain.
text: The system at it's current stage employs the neural networks and second stream, but the group leader would like the network investigated separately, incase it is hurting performance.
text: There are worries regarding the need to make adjustments so the new software can handle the group's different feature set.
text: The system, whilst improved, also has increased latency, and while the limit has not been set, the group need to reduce it.
text: Likewise the number of features the use in their system, since this has been set at an arbitrarily low value.
text: There has been an increase in the number of deletion in the errors, which is of some concern.
text: Speaker mn007 has been implementing a new voice activity detector on noise compensated data, and it performs much better.
text: He has also been working on the tandem neural network.
text: Speaker me013, along with a student, submitted work on reverberation for a speech workshop.
text: Speaker me018 has downloaded and compiled the software he was asked to work with in the previous meeting.