A: or should it should it be you know coronal instead of not coronal as it was labelled in the paper ? "
A: So I ha haven't hunted down all the all the mistakes yet ,
C: Uh - huh .
A: but C: But a as I was saying , people do get probabilities from these things ,
B: OK .
C: and and uh we were just trying to remember how they do ,
C: but people have used it for speech recognition , and they have gotten probabilities .
C: So they have some conversion from these distances to probabilities .
B: OK .
A: Right , yeah .
C: There 's you have you have the paper ,
C: right ?
C: The Mississippi State paper ?
A: Mm - hmm .
A: Mm - hmm .
C: Yeah , if you 're interested y you could look ,
B: And B: OK . OK .
A: Yeah ,
C: yeah .
A: I can I can show you I A: yeah ,
E: So in your in in the thing that you 're doing , uh you have a vector of ones and zeros for each phone ?
A: our A: Mm - hmm .
A: Uh , is this the class project ,
A: or ?
E: Yeah .
A: OK .
A: um
E: Is that what you 're A: Right , <comment> Right , right
A: f so for every phone there is there is a um a vector of ones and zeros f uh corresponding to whether it exhibits a particular phonological feature or not .
E: Mm - hmm .
E: Mm - hmm .
E: And so when you do your
E: wh I 'm what is the task for the class project ?
E: To come up with the phones ?
A: Um
E: or to come up with these vectors to see how closely they match the phones ,
A: Oh .
A: Right ,
A: um to come up with a mapping from um MFCC 's or s some feature set , um to uh w to whether there 's existence of a particular phonological feature .
E: or ?
E: Mm - hmm .
A: And um
A: yeah ,
A: basically it 's to learn a mapping from  from the MFCC 's to uh phonological features .
A: Is it did that answer your question ?
E: I think so .
A: OK .
E: I guess I mean , uh I 'm not sure what you what you 're what you get out of your system .
A: C
E: Do you get out a uh a vector of these ones and zeros and then try to find the closest matching phoneme to that vector ,
A: Mm - hmm .
E: or ?
A: Oh .
A: No , no .
A: I 'm not I 'm not planning to do any any phoneme mapping yet .
A: Just  it 's it 's basically it 's it 's really simple , basically a detection of phonological features .
E: Uh - huh .
E: I see .
A: Yeah ,
A: and um  cuz the uh A: So King and and Taylor um did this with uh recurrent neural nets ,
E: Yeah .
E: Mm - hmm .
A: and this i their their idea was to first find a mapping from MFCC 's to uh phonological features
A: and then later on , once you have these phonological features , then uh map that to phones .
E: Mm - hmm .
A: So I 'm I 'm sort of reproducing phase one of their stuff .
E: Mmm .
E: So they had one recurrent net for each particular feature ?
A: Right .
A: Right . Right . Right .
E: I see .
E: I wo did they compare that I mean , what if you just did phone recognition and did the reverse lookup .
A: Uh .
E: So you recognize a phone and which ever phone was recognized , you spit out it 's vector of ones and zeros .
A: Mm - hmm .
C: I expect you could do that .
E: I mean uh A: Uh .
C: That 's probably not what he 's going to do on his class project .
E: Yeah .
C: Yeah .
E: No .
C: So um have you had a chance to do this um thing we talked about yet with the uh A: Yeah .
C: um
E: Insertion penalty ?
C: Uh . No actually I was going a different C: That 's a good question , too ,
C: but I was gonna ask about the  the um changes to the data in comparing PLP and mel cepstrum for the SRI system .
E: Uh .
E: Well what I 've been E: " Changes to the data " ,
E: I 'm not sure I C: Right .
C: So we talked on the phone about this , that that there was still a difference of a of a few percent
E: Yeah .
E: Right .
C: and you told me that there was a difference in how the normalization was done .
C: And I was asking if you were going to do  redo it uh for PLP with the normalization done as it had been done for the mel cepstrum .
E: Mm - hmm .
E: Uh right ,
E: no I haven't had a chance to do that .
E: What I 've been doing is uh trying to figure out C: OK .
E: it just seems to me like there 's a um E: well it seems like there 's a bug ,
E: because the difference in performance is it 's not gigantic
E: but it 's big enough that it it seems wrong .
C: Yeah ,
C: I agree ,
E: and C: but I thought that the normalization difference was one of the possibilities ,
E: Yeah , but I don't I 'm not C: right ?
E: Yeah ,
E: I guess I don't think that the normalization difference is gonna account for everything .
E: So what I was working on is um just going through and checking the headers of the wavefiles ,
C: OK .
E: to see if maybe there was a um a certain type of compression or something that was done that my script wasn't catching .
E: So that for some subset of the training data , uh the the the features I was computing were junk .
C: OK .
E: Which would you know cause it to perform OK ,
E: but uh , you know , the the models would be all messed up .
E: So I was going through and just double - checking that kind of think first , to see if there was just some kind of obvious bug in the way that I was computing the features .
C: Mm - hmm .
C: I see .
C: OK .
E: Looking at all the sampling rates to make sure all the sampling rates were what eight K , what I was assuming they were ,
C: Yeah .
E: um C: Yeah , that makes sense , to check all that .
E: Yeah .
E: So I was doing that first , before I did these other things , just to make sure there wasn't something C: Although really , uh uh , a couple three percent uh difference in word error rate uh <comment> could easily come from some difference in normalization , I would think .
C: But
E: Yeah ,
E: and I think , hhh <comment> I 'm trying to remember but I think I recall that Andreas was saying that he was gonna run sort of the reverse experiment .
E: Uh which is to try to emulate the normalization that we did
E: but with the mel cepstral features .
E: Sort of , you know , back up from the system that he had .
E: I thought he said he was gonna E: I have to look back through my my email from him .
C: Yeah , he 's probably off at at uh his meeting now ,
E: Yeah ,
E: he 's gone now .
E: Um .
C: yeah .
C: Yeah .
C: But
C: yeah
C: the I sh think they should be roughly equivalent ,
E: But C: um
C: I mean again the Cambridge folk found the PLP actually to be a little better .
E: Right .
C: Uh So it 's  um
C: I mean the other thing I wonder about was whether there was something just in the the bootstrapping of their system which was based on C: but maybe not , since they E: Yeah see one thing that 's a little bit um E: I was looking I 've been studying and going through the logs for the system that um Andreas created .
E: And um his uh the way that the  <comment> S R I system looks like it works is that it reads the wavefiles directly ,
E: uh and does all of the cepstral computation stuff on the fly .
C: Right .
C: Right .
E: And , so there 's no place where these where the cepstral files are stored , anywhere that I can go look at and compare to the PLP ones ,
E: so whereas with our features , he 's actually storing the cepstrum on disk , and he reads those in .
C: Right .
E: But it looked like he had to give it uh
E: even though the cepstrum is already computed , he has to give it uh a front - end parameter file .
E: Which talks about the kind of uh com computation that his mel cepstrum thing does ,
C: Uh - huh .
E: so i I I don't know if that it probably doesn't mess it up ,
E: it probably just ignores it if it determines that it 's already in the right format or something
E: but the the the two processes that happen are a little different .
E: So .
C: Yeah .
C: So anyway , there 's stuff there to sort out .
E: Yeah .
E: Yeah .
C: So ,
C: OK .
C: Let 's go back to what you thought I was asking you .
E: Yeah
E: no and I didn't have a chance to do that .
C: Ha !
C: Oh !
C: You had the sa same answer anyway .
E: Yeah .
E: Yeah . I 've been um , I 've been working with um Jeremy on his project
E: and then I 've been trying to track down this bug in uh the ICSI front - end features .
C: Uh - huh .
E: So one thing that I did notice , yesterday I was studying the um the uh RASTA code
C: Uh - huh .
E: and it looks like we don't have any way to um control the frequency range that we use in our analysis .
E: We basically it looks to me like we do the FFT , um and then we just take all the bins