E: Oh .
E: Mm - hmm .
C: I I think s I think support vector stuff sort of goes back to  to that kind of thing .
E: I see .
C: Um .
E: So rather than doing nearest neighbor where you compare to every single one , you just pick a few critical ones ,
E: and C: Yeah .
E: Hmm .
C: And th the
C: You know , um neural net approach uh or Gaussian mixtures for that matter are sort of fairly brute force kinds of things , where you sort of  you predefine that there is this big bunch of parameters
C: and then you you place them as you best can to define the boundaries ,
C: and in fact , as you know , these things do take a lot of parameters
C: and and uh if you have uh only a modest amount of data , you have trouble uh learning them .
C: Um , so I I guess the idea to this is that it it is reputed to uh be somewhat better in that regard .
E: Mm - hmm .
A: Right .
A: I it can be a a reduced um parameterization of of the the model by just keeping certain selected examples .
E: Hmm .
A: Yeah .
A: So .
C: But I don't know if people have done sort of careful comparisons of this on large tasks or anything .
C: Maybe maybe they have .
C: I don't know .
A: Yeah , I don't know either .
C: Yeah .
B: S do you get some kind of number between zero and one at the output ?
A: Actually you don't get a you don't get a nice number between zero and one .
A: You get you get either a zero or a one .
A: Um , uh
A: there are there are pap
A: Well , basically , it 's it 's um you you get a distance measure at the end of the day ,
A: and then that distance measure is is um  is translated to a zero or one .
A: Um .
C: But that 's looking at it for for classification for binary classification ,
A: That 's for classification ,
E: And you get that for each class , you get a zero or a one .
C: right ?
A: right .
A: Right .
C: But you have the distances to work with .
A: You have the distances to work with ,
C: Cuz actually Mississippi State people did use support vector machines for uh uh speech recognition and they were using it to estimate probabilities .
A: yeah .
A: Yeah .
A: Yeah , they  they had a had a way to translate the distances into into probabilities with the with the simple um uh sigmoidal function .
C: Yeah ,
C: and d did they use sigmoid or a softmax type thing ?
C: And didn't they like exponentiate or something
A: Um  Yeah ,
C: and then divide by the sum of them ,
A: there 's some there 's like one over one plus the exponential or something like that .
C: or ?
C: Oh it i
C: Oh , so it is a sigmoidal .
A: Yeah .
C: OK .
C: Alright .
E: Did the did they get good results with that ?
C: I mean , they 're OK ,
C: I I don't I don't think they were earth earth shattering ,
C: but I think that uh this was a couple years ago ,
E: Hmm .
C: I remember them doing it at some meeting ,
C: and and um I don't think people were very critical because it was interesting just to to try this
C: and you know , it was the first time they tried it ,
C: so  so the you know , the numbers were not incredibly good
E: Hmm .
C: but there 's you know , it was th reasonable .
E: Mm - hmm .
C: I I don't remember anymore .
C: I don't even remember what the task was ,
C: it <comment> was Broadcast News , or something .
C: I don't know .
E: Hmm .
A: Right .
B: Uh s So Barry , if you just have zero and ones , how are you doing the speech recognition ?
A: Oh
A: I 'm not do I 'm not planning on doing speech recognition with it .
A: I 'm just doing detection of phonological features .
B: Oh . OK .
A: So uh for example , this this uh feature set called the uh sound patterns of English um is just a bunch of um binary valued features .
A: Let 's say , is this voicing , or is this not voicing ,
A: is this sonorants , not sonorants ,
A: and stuff like that .
B: OK .
A: So .
E: Did you find any more mistakes in their tables ?
A: Oh !
A: Uh I haven't gone through the entire table , yet .
A: Yeah ,
A: yesterday I brought Chuck the table
A: and I was like , " wait , this is Is the mapping from N to to this phonological feature called um " coronal " ,
A: is is should it be shouldn't it be a one ?