Professor C: So um have you had a chance to do this um thing we talked about yet with the uh um
PhD E: Insertion penalty ?
Professor C: Uh . No actually I was going a different That 's a good question , too , but I was gonna ask about the  the um changes to the data in comparing PLP and mel cepstrum for the SRI system .
PhD E: Uh . Well what I 've been " Changes to the data " , I 'm not sure I Professor C: Right . So we talked on the phone about this , that that there was still a difference of a of a few percent
PhD E: Yeah . Right .
Professor C: and you told me that there was a difference in how the normalization was done . 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 .
PhD E: Mm - hmm . Uh right , no I haven't had a chance to do that .
Professor C: OK .
PhD E: What I 've been doing is uh trying to figure out it just seems to me like there 's a um well it seems like there 's a bug , because the difference in performance is it 's not gigantic but it 's big enough that it it seems wrong .
Professor C: Yeah , I agree , but I thought that the normalization difference was one of the possibilities ,
PhD E: and Yeah , but I don't I 'm not Professor C: right ?
PhD E: Yeah , I guess I don't think that the normalization difference is gonna account for everything .
Professor C: OK .
PhD E: So what I was working on is um just going through and checking the headers of the wavefiles , to see if maybe there was a um a certain type of compression or something that was done that my script wasn't catching . So that for some subset of the training data , uh the the the features I was computing were junk .
Professor C: OK .
PhD E: Which would you know cause it to perform OK , but uh , you know , the the models would be all messed up . 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 .
Professor C: Mm - hmm . I see . OK .
PhD E: Looking at all the sampling rates to make sure all the sampling rates were what eight K , what I was assuming they were ,
Professor C: Yeah .
PhD E: um Professor C: Yeah , that makes sense , to check all that .
PhD E: Yeah . So I was doing that first , before I did these other things , just to make sure there wasn't something Professor 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 . But
PhD E: Yeah , 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 . Uh which is to try to emulate the normalization that we did but with the mel cepstral features . Sort of , you know , back up from the system that he had . I thought he said he was gonna I have to look back through my my email from him .
Professor C: Yeah , he 's probably off at at uh his meeting now ,
PhD E: Yeah , he 's gone now .
Professor C: yeah .
PhD E: Um .
Professor C: Yeah . But yeah
PhD E: But Professor C: the I sh think they should be roughly equivalent , um I mean again the Cambridge folk found the PLP actually to be a little better . Uh So it 's  um
PhD E: Right .
Professor 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 but maybe not , since they PhD E: Yeah see one thing that 's a little bit um I was looking I 've been studying and going through the logs for the system that um Andreas created . And um his uh the way that the  {comment} S R I system looks like it works is that it reads the wavefiles directly , uh and does all of the cepstral computation stuff on the fly .
Professor C: Right . Right .
PhD 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 , so whereas with our features , he 's actually storing the cepstrum on disk , and he reads those in .
Professor C: Right .
PhD E: But it looked like he had to give it uh even though the cepstrum is already computed , he has to give it uh a front - end parameter file . Which talks about the kind of uh com computation that his mel cepstrum thing does ,
Professor C: Uh - huh .
PhD E: so i I I don't know if that it probably doesn't mess it up , it probably just ignores it if it determines that it 's already in the right format or something but the the the two processes that happen are a little different .
Professor C: Yeah .
PhD E: So .
Professor C: So anyway , there 's stuff there to sort out .
PhD E: Yeah . Yeah .
Professor C: So , OK . Let 's go back to what you thought I was asking you .
PhD E: Yeah no and I didn't have a chance to do that .
Professor C: Ha ! Oh ! You had the sa same answer anyway .
PhD E: Yeah . Yeah . I 've been um , I 've been working with um Jeremy on his project and then I 've been trying to track down this bug in uh the ICSI front - end features .
Professor C: Uh - huh .
PhD E: So one thing that I did notice , yesterday I was studying the um the uh RASTA code
Professor C: Uh - huh .
PhD E: and it looks like we don't have any way to um control the frequency range that we use in our analysis . We basically it looks to me like we do the FFT , um and then we just take all the bins and we use everything . We don't have any set of parameters where we can say you know , " only process from you know a hundred and ten hertz to thirty - seven - fifty " .
Professor C: Um PhD E: At least I couldn't see any kind of control for that .
Professor C: Yeah , I don't think it 's in there , I think it 's in the uh uh uh the filters . So , the F F T is on everything , but the filters um , for instance , ignore the the lowest bins and the highest bins . And what it does is it it copies
PhD E: The the filters ? Which filters ?
Professor C: um The filter bank which is created by integrating over F F T bins .
PhD E: Mm - hmm .
Professor C: um
PhD E: When you get the mel When you go to the mel scale .
Professor C: Right . Yeah , it 's bark scale , and it 's it it um it actually copies the uh um the second filters over to the first . So the first filters are always and you can s you can specify a different number of uh features different number of filters , I think , as I recall . So you can specify a different number of filters , and whatever um uh you specify , the last ones are gonna be ignored . So that that 's a way that you sort of change what the what the bandwidth is . Y you can't do it without I think changing the number of filters , but PhD E: I saw something about uh that looked like it was doing something like that , but I didn't quite understand it . So maybe Professor C: Yeah , so the idea is that the very lowest frequencies and and typically the veriest {comment} highest frequencies are kind of junk .
PhD E: Uh - huh .
Professor C: And so um you just for continuity you just approximate them by  by the second to highest and second to lowest . It 's just a simple thing we put in .
PhD E: Mm - hmm .
Professor C: And and so if you h
PhD E: But so the but that 's a fixed uh thing ?
Professor C: Yeah , {comment} I think that 's a fixed thing .
PhD E: There 's nothing that lets you Professor C: But see see my point ? If you had  If you had ten filters , then you would be throwing away a lot at the two ends .
PhD E: Mm - hmm .
Professor C: And if you had if you had fifty filters , you 'd be throwing away hardly anything .
PhD E: Mm - hmm .
Professor C: Um , I don't remember there being an independent way of saying " we 're just gonna make them from here to here " .
PhD E: Use this analysis bandwidth or something .
Professor C: But I I I don't know , it 's actually been awhile since I 've looked at it .
PhD E: Yeah , I went through the Feacalc code and then looked at you know just calling the RASTA libs {comment} and thing like that . And I didn't I couldn't see any wh place where that kind of thing was done . But um I didn't quite understand everything that I saw ,
Professor C: Yeah , see I don't know Feacalc at all .
PhD E: so Mm - hmm .
Professor C: But it calls RASTA with some options , and um
PhD E: Right .
Professor C: But I I think in I don't know . I guess for some particular database you might find that you could tune that and tweak that to get that a little better , but I think that in general it 's not that critical . I mean there 's PhD E: Yeah .
Professor C: You can You can throw away stuff below a hundred hertz or so and it 's just not going to affect phonetic classification at all .
PhD E: Another thing I was thinking about was um is there a I was wondering if there 's maybe um certain settings of the parameters when you compute PLP which would basically cause it to output mel cepstrum . So that , in effect , what I could do is use our code but produce mel cepstrum and compare that directly to Professor C: Well , it 's not precisely . Yeah . I mean ,
PhD E: Hmm .
Professor C: um , um what you can do is um you can definitely change the the filter bank from being uh a uh trapezoidal integration to a a a triangular one ,
PhD E: Mm - hmm .
Professor C: which is what the typical mel mel cepstral uh filter bank does .
PhD E: Mm - hmm .
Professor C: And some people have claimed that they got some better performance doing that , so you certainly could do that easily . But the fundamental difference , I mean , there 's other small differences PhD E: There 's a cubic root that happens , right ?
Professor C: Yeah , but , you know , as opposed to the log in the other case . I mean the fundamental d d difference that we 've seen any kind of difference from before , which is actually an advantage for the P L P i uh , I think , is that the the smoothing at the end is auto - regressive instead of being cepstral uh , {comment} from cepstral truncation . So um it 's a little more noise robust .
PhD E: Hmm .
Professor C: Um , and that 's that 's why when people started getting databases that had a little more noise in it , like like uh um Broadcast News and so on , that 's why c Cambridge switched to PLP I think .
PhD E: Mm - hmm .
Professor C: So um That 's a difference that I don't think we put any way to get around , since it was an advantage . um uh
PhD E: Mm - hmm .
Professor C: but we did eh we did hear this comment from people at some point , that um it uh they got some better results with the triangular filters rather than the trapezoidal . So that is an option in RASTA .
PhD E: Hmm .
Professor C: Uh and you can certainly play with that . But I think you 're probably doing the right thing to look for bugs first . I don't know .
PhD E: Yeah just it just seems like this kind of behavior could be caused by you know s some of the training data being messed up .
Professor C: Could be .
PhD E: You know , you 're sort of getting most of the way there , but there 's a So I started going through and looking One of the things that I did notice was that the um log likelihoods coming out of the log recognizer from the PLP data were much lower , much smaller , than for the mel cepstral stuff , and that the average amount of pruning that was happening was therefore a little bit higher for the PLP features .
Professor C: Oh - huh !
PhD E: So , since he used the same exact pruning thresholds for both , I was wondering if it could be that we 're getting more pruning .
Professor C: Oh ! He he  He used the identical pruning thresholds even though the s the range of p of the likeli
PhD E: Yeah .
Professor C: Oh well that 's  That 's a pretty good {comment} point right there .
PhD E: Right . Right .
Professor C: Yeah .
PhD E: Yeah ,
Professor C: I would think that you might wanna do something like uh you know , look at a few points to see where you are starting to get significant search errors .
PhD E: so That 's Right . Well , what I was gonna do is I was gonna take um a couple of the utterances that he had run through , then run them through again but modify the pruning threshold and see if it you know , affects the score .
Professor C: Yeah . Yeah . But I mean you could uh if if if that looks promising you could , you know , r uh run the overall test set with a with a few different uh pruning thresholds for both ,
PhD E: So . Mm - hmm .
Professor C: and presumably he 's running at some pruning threshold that 's that 's uh , you know gets very few search errors
PhD E: Right .
Professor C: but is is relatively fast
PhD E: Mm - hmm . Right . I mean , yeah , generally in these things you you turn back pruning really far ,
Professor C: and PhD E: so I I didn't think it would be that big a deal because I was figuring well you have it turned back so far that you know it Professor C: But you may be in the wrong range for the P L P features for some reason .
PhD E: Yeah . Yeah . Yeah . And the uh the the run time of the recognizer on the PLP features is longer which sort of implies that the networks are bushier , you know , there 's more things it 's considering which goes along with the fact that the matches aren't as good . So uh , you know , it could be that we 're just pruning too much .
Professor C: Yeah .
PhD E: So .
Professor C: Yeah , maybe just be different kind of distributions and and
PhD E: Mm - hmm .
Professor C: yeah so that 's another possible thing . They they should really shouldn't PhD E: Mm - hmm .
Professor C: There 's no particular reason why they would be exactly behave exactly the same .
PhD E: Mm - hmm . Right . Right .
Professor C: So .
PhD E: So . There 's lots of little differences .
Professor C: Yeah .
PhD E: So . Uh .
Professor C: Yeah .
PhD E: Trying to track it down .
Professor C: Yeah . I guess this was a little bit off topic , I guess , because I was I was thinking in terms of th this as being a a a a core item that once we once we had it going we would use for a number of the front - end things also .
PhD E: Yeah
Professor C: So .
PhD E: Mm - hmm .
Professor C: um Wanna Grad B: That 's as far as my stuff goes ,