B: Could you cluster the just do some kind of clustering ?
A: Guess you could , yeah .
B: Bin them up into different categories and A: Yeah .
A: So anyway that 's that 's uh that 's another whole direction that cou could be looked at .
A: Um . Um . I mean in general it 's gonna be for new data that you look at ,
A: it 's gonna be hidden variable because we 're not gonna get everybody sitting in these meetings to wear the pellets and E: Right .
A: Um . So .
E: Right .
B: So you 're talking about using that data to get
B: uh
B: instead of using canonical mappings of phones .
E: Right .
B: So you 'd use that data to give you sort of what the  the true mappings are for each phone ?
E: Mm - hmm .
B: I see .
E: Mm - hmm .
A: Yeah .
A: So wh yeah , where this fits into the rest in in my mind , I guess , is that um we 're looking at different ways that we can combine uh different kinds of of rep front - end representations um in order to get robustness under difficult or even , you know , typical conditions .
A: And part of it , this robustness , seems to come from uh multi - stream or multi - band sorts of things
A: and Saul seems to have a reasonable way of looking at it , at least for one   one um articulatory feature .
A: The question is is can we learn from that to change some of the other methods we have , since A: I mean , one of the things that 's nice about what he had I thought was that that it it um A: the decision about how strongly to train the different pieces is based on uh a a reasonable criterion with hidden variables
A: rather than um just assuming that you should train e e every detector uh with equal strength towards uh it being this phone or that phone .
B: Hmm .
A: Right ?
A: So it so um he 's got these um uh uh
A: he " AND 's " between these different features .
A: It 's a soft " AND " , I guess
A: but in in principle you you wanna get a strong concurrence of all the different things that indicate something
A: and then he " OR 's " across the different soft " OR 's " across the different uh multi - band channels .
A: And um the weight
A: yeah , the target for the training of the " AND " " AND ' ed " things is something that 's kept uh as a hidden variable ,
A: and is learned with EM .
B: So he doesn't have A: Whereas what we were doing is is uh taking the phone target and then just back propagating from that
A: which means that it 's  it 's uh i It could be for instance that for a particular point in the data you don't want to um uh train a particular band train the detectors for a particular band .
A: You you wanna ignore that band , cuz that 's a Ban - band is a noisy noisy measure .
B: Mm - hmm .
A: And we don't A: We 're we 're still gonna try to train it up .
A: In our scheme we 're gonna try to train it up to do as well well as it can at predicting .
A: Uh . Maybe that 's not the right thing to do .
B: So he doesn't have to have truth marks
B: or Ho
E: F right ,
E: and uh he doesn't have to have hard labels .
A: Well at the at the tail end , yeah , he has to know what 's where it 's sonorant .
E: Right .
E: For the full band .
A: But he 's but what he 's - but what he 's not training up uh what he doesn't depend on as truth is
A: um I guess one way of describing would be
A: if if a sound is sonorant is it sonorant in this band ?
A: Is it sonorant in that band ?
A: Is it sonorant in that band ?
E: Right .
A: i It 's hard to even answer that
A: what you really mean is that the whole sound is sonorant .
B: Mm - hmm .
A: So
B: OK .
A: then it comes down to , you know , to what extent should you make use of information from particular band towards making your decision .
B: I see .
A: And um uh we 're making in a sense sort of this hard decision that you should you should use everything uh with with uh equal strength .
A: And uh because in the ideal case we would be going for posterior probabilities ,
A: if we had uh enough data to really get posterior probabilities
A: and if the if we also had enough data so that it was representative of the test data
A: then we would in fact be doing the right thing to train everything as hard as we can .
A: But um this is something that 's more built up along an idea of robustness from from the beginning