PhD A: So , how do you know that what you have is too much if they 're still deciding ?
Professor B: Uh , we don't , but it 's just I mean , the main thing is that since that we got burned last time , and you know , by not worrying about it very much , we 're just staying conscious of it .
PhD A: Uh - huh . Oh , OK , I see .
Professor B: And so , th I mean , if if 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 . So PhD A: Ah , OK .
Professor B: Uh PhD A: But still , that 's that 's a pretty big , uh , win . And it doesn't seem like you 're in terms of your delay , you 're , uh , that Professor B: He added a bit on , I guess , because before we were we were had were able to have the noise , uh , stuff , uh , and the LVA be in parallel .
PhD C: Hmm .
Professor B: And now he 's he 's requiring it to be done first .
PhD C: Well , but I think the main thing , maybe , is the cleaning of the speech , which takes forty milliseconds or so .
Professor B: Right . Well , so you say let 's say ten milliseconds seconds for the LDA .
PhD C: And and but the LDA is , well , pretty short right now .
Professor B: Well , ten . And then forty for the other .
PhD C: Yeah .
PhD D: Yeah , the LDA LDA we don't know , is , like is it very crucial for the features , right ?
PhD C: No . I just This is the first try .
PhD D: Yeah .
Professor B: Right ,
PhD C: I mean , I maybe the LDA 's not very useful then .
Professor B: so you could start pulling back ,
PhD D: S s h
Professor B: but PhD D: Yeah ,
Professor B: But I think you have PhD D: l
Professor B: I mean , you have twenty for delta computation which y now you 're sort of doing twice , right ? But yo w were you doing that before ?
PhD C: Mmm . Well , in the proposal , um , the input of the VAD network were just three frames , I think .
PhD D: On the in the Mm - hmm . Just Yeah , just the static , no delta .
Professor B: Right .
PhD C: Uh , static features .
Professor B: So , what you have now is fort uh , forty for the the noise , twenty for the delta , and ten for the LDA . That 's seventy milliseconds of stuff which was formerly in parallel ,
PhD C :
Professor B: right ? So I think ,
PhD C: Mm - hmm .
Professor B: you know , that 's that 's the difference as far as the timing , right ?
PhD C: Yeah .
Professor B: Um , and you could experiment with cutting various pieces of these back a bit , but I mean , we 're s we 're not we 're not in terrible shape .
PhD A: Yeah , that 's what it seems like to me . It 's pretty good .
Professor B: Yeah .
PhD C: Mm - hmm .
Professor B: It 's it 's not like it 's adding up to four hundred milliseconds or something .
PhD A: Where where is this where is this fifty - seven point O two in in comparison to the last evaluation ?
Professor B: Well , it 's I think it 's better than anything , uh , anybody got .
PhD A: Oh , is that right ?
PhD C: Yeah . The best was fifty - four point five .
Professor B: Yeah .
PhD D: Point s
PhD A: Oh .
Professor B: Yeah . Uh
PhD C: And our system was forty - nine , but with the neural network .
PhD A: Wow . So this is almost ten percent .
Professor B: With the f with the neural net . Yeah , and r and PhD C: It would
PhD D: Yeah , so this is this is like the first proposal . The proposal - one . It was forty - four , actually .
Professor B: Yeah . Yeah . And we still don't have the neural net in . So so it 's PhD A: Wow .
Professor B: You know . So it 's We 're we 're doing better .
PhD A: This is this is really good .
Professor B: I mean , we 're getting better recognition . I mean , I 'm sure other people working on this are not sitting still either , but PhD A: Yeah .
Professor B: but but , uh Uh , I mean , the important thing is that we learn how to do this better , and , you know . So . Um , Yeah . So , our , um Yeah , you can see the kind of kind of numbers that we 're having , say , on SpeechDat - Car which is a hard task , cuz it 's really , um I think it 's just sort of sort of reasonable numbers , starting to be . I mean , it 's still terri
PhD C: Mm - hmm . Yeah , even for a well - matched case it 's sixty percent error rate reduction ,
Professor B: Yeah .
PhD C: which is Professor B: Yeah . Probably half . Good !
PhD C: Um , Yeah . So actually , this is in between what we had with the previous VAD and what Sunil did with an IDL VAD . Which gave sixty - two percent improvement , right ?
PhD D: Yeah , it 's almost that .
PhD C: So PhD D: It 's almost an average somewhere around PhD C: Yeah .
PhD D: Yeah .
PhD A: What was that ? Say that last part again ?
PhD C: So , if you use , like , an IDL VAD , uh , for dropping the frames ,
PhD D: o o Or the best we can get .
PhD C: the best that we can get i That means that we estimate the silence probability on the clean version of the utterances . Then you can go up to sixty - two percent error rate reduction , globally .
PhD A: Mmm .
PhD C: Mmm Yeah .
PhD A: So that would be even That wouldn't change this number down here to sixty - two ?
PhD C: Yeah .
Professor B: Yeah . So you you were get
PhD C: If you add a g good v very good VAD , that works as well as a VAD working on clean speech ,
PhD A: Yeah . Yeah .
PhD C: then you wou you would go PhD A: So that 's sort of the best you could hope for .
PhD C: Mm - hmm .
PhD A: I see .
Professor B: Probably . Yeah . So fi si fifty - three is what you were getting with the old VAD .
PhD C: Yeah .
Professor B: And , uh and sixty - two with the the , you know , quote , unquote , cheating VAD . And fifty - seven is what you got with the real VAD .
PhD C: Mm - hmm .
Professor B: That 's great .
PhD C: Uh , yeah , the next thing is , I started to play Well , I don't want to worry too much about the delay , no . Maybe it 's better to wait
Professor B: OK .
PhD C: for the decision
Professor B: Yeah .
PhD C: from the committee . Uh , but I started to play with the , um ,  uh , tandem neural network . Mmm I just did the configuration that 's very similar to what we did for the February proposal . And Um . So . There is a f a first feature stream that use uh straight MFCC features .
Professor B: Mm - hmm .
PhD C: Well , these features actually . And the other stream is the output of a neural network , using as input , also , these , um , cleaned MFCC . Um PhD A: Those are th those are th what is going into the tandem net ?
PhD C: I don't have the comp Mmm ?
PhD A: Those two ?
PhD C: So there is just this feature stream , {comment} the fifteen MFCC plus delta and double - delta .
Professor B: No .
PhD A: Yeah ?
PhD C: Um , so it 's makes forty - five features {comment} that are used as input to the HTK . And then , there is there are more inputs that comes from the tandem MLP .
PhD A: Oh , oh . OK . I see .
Professor B: Yeah , h he likes to use them both ,
PhD A: Uh - huh .
Professor B: cuz then it has one part that 's discriminative ,
PhD C: Yeah . Um Professor B: one part that 's not .
PhD A: Right . OK .
PhD C: So , um , uh , yeah . Right now it seems that i I just tested on SpeechDat - Car while the experiment are running on your on TI - digits . Well , it improves on the well - matched and the mismatched conditions , but it get worse on the highly mismatched . Um ,
PhD A: Compared to these numbers ?
PhD C: Compared to these numbers , yeah . Um ,
Professor B: y
PhD C: like , on the well - match and medium mismatch , the gain is around five percent relative , but it goes down a lot more , like fifteen percent on the HM case .
Professor B: You 're just using the full ninety features ?
PhD C: The Professor B: Y you have ninety features ?
PhD C: i I have , um From the networks , it 's twenty - eight . So Professor B: And from the other side it 's forty - five .
PhD C: So , d i It 's forty - five .
Professor B: So it 's you have seventy - three features ,
PhD C: Yeah .
Professor B: and you 're just feeding them like that .
PhD C: Yeah .
Professor B: There isn't any KLT or anything ?
PhD C: Mm - hmm . There 's a KLT after the neural network , as as before .
PhD A: That 's how you get down to twenty - eight ?
PhD C: Yeah .
PhD A: Why twenty - eight ?
PhD C: I don't know .
PhD A: Oh .
PhD C: Uh . It 's i i i It 's because it 's what we did for the first proposal . We tested , uh , trying to go down
PhD A: Ah .
Professor B: It 's a multiple of seven .
PhD C: and Yeah .
PhD D: Yeah .
PhD C: So Um .
PhD D: Yeah .
PhD C: I wanted to do something very similar to the proposal as a first first try .
PhD D: Yeah .
PhD A: I see .
Professor B: Yeah .
PhD A: Yeah . That makes sense .
PhD C: But we have to for sure , we have to go down , because the limit is now sixty features .
Professor B: Yeah .
PhD C: So , uh , we have to find a way to decrease the number of features . Um PhD A: So , it seems funny that I don't know , maybe I don't u quite understand everything , {comment} but that adding features I guess I guess if you 're keeping the back - end fixed . Maybe that 's it . Because it seems like just adding information shouldn't give worse results . But I guess if you 're keeping the number of Gaussians fixed in the recognizer , then Professor B: Well , yeah .
PhD C: Mmm .
Professor B: But , I mean , just in general , adding information Suppose the information you added , well , was a really terrible feature and all it brought in was noise .
PhD A: Yeah .
Professor B: Right ? So so , um Or or suppose it wasn't completely terrible , but it was completely equivalent to another one feature that you had , except it was noisier .
PhD A: Uh - huh .
Professor B: Right ? In that case you wouldn't necessarily expect it to be better at all .
PhD A: Oh , yeah , I wasn't necessarily saying it should be better . I 'm just surprised that you 're getting fifteen percent relative worse on the wel
Professor B: Uh - huh .
PhD C: But it 's worse .
Professor B: On the highly mismatched condition .
PhD A: On the highly mismatch .
PhD C: Yeah , I PhD A: Yeah .
Professor B: So , " highly mismatched condition " means that in fact your training is a bad estimate of your test .
PhD C: Uh - huh .
Professor B: So having 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 . I mean , you 're right . If you have if you have , uh , lots and lots of data , and you have and your your your training is representative of your test , then getting more sources of information should just help . But but it 's It doesn't necessarily work that way .
PhD A: Huh .
PhD C: Mm - hmm .