B: It 's it 's , you know , you have the posterior combination
B: but then you get the features from that and use them as a feature combination with these these other things .
B: And that seemed , at least in the last one , as he was just saying , he he when he only did discriminative stuff , i it actually was was it didn't help at all in this particular case .
A: Yeah .
B: There was enough of a difference , I guess , between the testing and training .
B: But by having them both there B: The fact is some of the time , the discriminative stuff is gonna help you .
A: Mm - hmm .
B: And some of the time it 's going to hurt you ,
B: and by combining two information sources if , you know if if A: Right .
A: So you wouldn't necessarily then want to do LDA on the non - tandem features because now you 're doing something to them that B: That i i
B: I think that 's counter to that idea .
A: Yeah ,
B: Now , again , it 's we 're just trying these different things .
A: right .
B: We don't really know what 's gonna work best .
B: But if that 's the hypothesis , at least it would be counter to that hypothesis to do that .
A: Right .
B: Um ,
B: and in principle you would think that the neural net would do better at the discriminant part than LDA .
A: Right .
A: Yeah .
B: Though , maybe not .
A: Well y
A: Yeah .
A: Exactly .
A: I mean , we , uh we were getting ready to do the tandem , uh , stuff for the Hub - five system ,
A: and , um , Andreas and I talked about it ,
A: and the idea w the thought was , " Well , uh , yeah , that i you know th the neural net should be better ,
A: 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 ,
A: so that we can you know , show a clear path .
B: Right .
A: You know , that you have it without it ,
A: then you have the LDA ,
A: then you have the neural net ,
A: and you can see , theoretically .
A: So .
A: I was just wondering A: I I B: Well , I think that 's a good idea .
A: Yeah .
B: Did did you do that
B: or tha that 's a A: Um . No .
A: That 's what that 's what we 're gonna do next
A: as soon as I finish this other thing .
B: Yeah .
A: So .
B: Yeah .
B: No , well , that 's a good idea .
B: I I B: i Yeah .
A: We just want to show .
A: I mean , it everybody believes it ,
B: Oh , no
B: it 's a g
A: but you know , we just B: No , no ,
B: but it might not not even be true .
B: I mean , it 's it 's it 's it 's it 's a great idea .
A: Yeah .
B: I mean , one of the things that always disturbed me , uh , in the the resurgence of neural nets that happened in the eighties was that , um , a lot of people Because neural nets were pretty easy to to use a lot of people were just using them for all sorts of things without , uh , looking at all into the linear , uh uh , versions of them .
A: Yeah .
A: Mm - hmm .
B: And , uh , people were doing recurrent nets but not looking at IIR filters ,
A: Yeah .
B: and You know , I mean , uh ,
B: so I think , yeah , it 's definitely a good idea to try it .
A: Yeah ,
A: and everybody 's putting that on their systems now ,
A: and so , I
A: that 's what made me wonder about this ,
B: Well , they 've been putting them in their systems off and on for ten years ,
A: but .
B: but but but , uh ,
A: Yeah , what I mean is it 's it 's like in the Hub - five evaluations , you know ,
A: and you read the system descriptions and everybody 's got , you know , LDA on their features .
B: And now they all have that .
B: I see .
A: And so .
B: Yeah .
A: Uh .
C: It 's the transformation they 're estimating on C: Well , they are trained on the same data as the final HMM are .
A: Yeah , so it 's different .
A: Yeah ,
A: exactly .
A: Cuz they don't have these , you know , mismatches that that you guys have .
C: Mm - hmm .
A: So that 's why I was wondering if maybe it 's not even a good idea .
C: Mm - hmm .
A: I don't know .
A: I I don't know enough about it ,
C: Mm - hmm .
A: but Um .
B: I mean , part of why B: I I think part of why you were getting into the KLT Y you were describing to me at one point that you wanted to see if , uh , you know , getting good orthogonal features was and combining the the different temporal ranges was the key thing that was happening or whether it was this discriminant thing , right ?