E: and we use everything .
E: 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 " .
C: Um E: At least I couldn't see any kind of control for that .
C: Yeah , I don't think it 's in there ,
C: I think it 's in the uh uh uh the filters .
C: So , the F F T is on everything ,
C: but the filters
C: um ,
C: for instance , ignore the the lowest bins and the highest bins .
C: And what it does is it it copies
E: The the filters ?
E: Which filters ?
C: um
C: The filter bank which is created by integrating over F F T bins .
E: Mm - hmm .
C: um
E: When you get the mel When you go to the mel scale .
C: Right .
C: Yeah ,
C: it 's bark scale ,
C: and it 's it it um it actually copies the uh um the second filters over to the first .
C: So the first filters are always C: and you can s you can specify a different number of uh features different number of filters ,
C: I think ,
C: as I recall .
C: So you can specify a different number of filters , and whatever um uh you specify , the last ones are gonna be ignored .
C: So that that 's a way that you sort of change what the what the bandwidth is .
C: Y you can't do it without I think changing the number of filters ,
E: I saw something about uh that looked like it was doing something like that , but I didn't quite understand it .
C: but E: So maybe C: Yeah , so the idea is that the very lowest frequencies and and typically the veriest <comment> highest frequencies are kind of junk .
E: Uh - huh .
C: And so um
C: you just for continuity you just approximate them by  by the second to highest and second to lowest .
E: Mm - hmm .
C: It 's just a simple thing we put in .
C: And and so if you h
E: But so the but that 's a fixed uh thing ?
E: There 's nothing that lets you C: Yeah , <comment> I think that 's a fixed thing .
C: But see see my point ?
C: If you had  If you had ten filters , then you would be throwing away a lot at the two ends .
E: Mm - hmm .
C: And if you had if you had fifty filters , you 'd be throwing away hardly anything .
E: Mm - hmm .
C: Um , I don't remember there being an independent way of saying " we 're just gonna make them from here to here " .
E: Use this analysis bandwidth or something .
C: But I I I don't know ,
C: it 's actually been awhile since I 've looked at it .
E: Yeah , I went through the Feacalc code and then looked at you know just calling the RASTA libs <comment> and thing like that .
E: And I didn't I couldn't see any wh place where that kind of thing was done .
E: But um I didn't quite understand everything that I saw ,
C: Yeah , see I don't know Feacalc at all .
E: so E: Mm - hmm .
C: But it calls RASTA with some options ,
C: and um
E: Right .
C: But I I think in C: I don't know .
C: I guess for some particular database you might find that you could tune that and tweak that to get that a little better ,
C: but I think that in general it 's not that critical .
C: I mean there 's E: Yeah .
C: You can You can throw away stuff below a hundred hertz or so
C: and it 's just not going to affect phonetic classification at all .
E: Another thing I was thinking about was um is there a E: 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 .
E: So that , in effect , what I could do is use our code
E: but produce mel cepstrum
E: and compare that directly to C: Well , it 's not precisely .
C: Yeah .
E: Hmm .
C: I mean ,
C: um , um
C: 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 ,
E: Mm - hmm .
C: which is what the typical mel mel cepstral uh filter bank does .
E: Mm - hmm .
C: And some people have claimed that they got some better performance doing that ,
C: so you certainly could do that easily .
C: But the fundamental difference ,
C: I mean , there 's other small differences E: There 's a cubic root that happens ,
E: right ?
C: Yeah ,
C: but , you know , as opposed to the log in the other case .
C: 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 .
C: So um it 's a little more noise robust .
E: Hmm .
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 ,
C: that 's why c Cambridge switched to PLP I think .
E: Mm - hmm .
C: So um
C: That 's a difference that I don't think we put any way to get around ,
C: since it was an advantage .
E: Mm - hmm .
C: um uh
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 .
C: So that is an option in RASTA .
E: Hmm .
C: Uh and you can certainly play with that .
C: But I think you 're probably doing the right thing to look for bugs first .
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 .
C: I don't know .
C: Could be .
E: You know , you 're sort of getting most of the way there , but there 's a E: So I started going through and looking E: 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 ,
E: 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 .
C: Oh - huh !
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 .
C: Oh !
C: He he  He used the identical pruning thresholds even though the s the range of p of the likeli
E: Yeah .
C: Oh well that 's  That 's a pretty good <comment> point right there .
E: Right .
E: Right .
C: Yeah .
E: Yeah ,
E: so 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 .
E: That 's E: Right .
E: Well , what I was gonna do is I was gonna take um a couple of the utterances that he had run through ,
E: then run them through again
E: but modify the pruning threshold and see if it you know , affects the score .
C: Yeah .
C: Yeah .
E: So .
C: 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 ,
E: Mm - hmm .
E: Right .
C: and presumably he 's running at some pruning threshold that 's that 's uh , you know gets very few search errors
C: but is is relatively fast
E: Mm - hmm .
E: Right . I mean , yeah , generally in these things you you turn back pruning really far ,
C: and E: so I I didn't think it would be that big a deal
E: because I was figuring well you have it turned back so far that you know it C: But you may be in the wrong range for the P L P features for some reason .
E: Yeah .
E: Yeah .
E: Yeah .
E: And the uh the the run time of the recognizer on the PLP features is longer
E: which sort of implies that the networks are bushier ,
E: you know , there 's more things it 's considering
E: which goes along with the fact that the matches aren't as good .
E: So uh , you know , it could be that we 're just pruning too much .
E: So .
C: Yeah .
C: Yeah , maybe just be different kind of distributions
C: and and
E: Mm - hmm .
C: yeah
C: so that 's another possible thing .
E: Mm - hmm .
C: They they should really shouldn't C: There 's no particular reason why they would be exactly behave exactly the same .
E: Mm - hmm . Right .
E: Right .
C: So .
E: So .
E: There 's lots of little differences .
E: So .
C: Yeah .
E: Uh .
C: Yeah .
E: Trying to track it down .
C: Yeah .
C: I guess this was a little bit off topic , I guess ,
E: Yeah
C: 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 .
E: Mm - hmm .
C: So .
C: um
C: Wanna B: That 's as far as my stuff goes ,
C: What 's what 's on B: yeah ,
C: Yeah .
B: well I tried this mean subtraction method .
B: Um .
B: Due to Avendano , I 'm taking s um six seconds of speech ,
B: um I 'm using two second FFT analysis frames , stepped by a half second
B: so it 's a quarter length step
B: and I  I take that frame and four f the four I take B: Sorry ,
B: I take the current frame and the four past frames and the four future frames
B: and that adds up to six seconds of speech .
B: And I calculate um the spectral mean , of the log magnitude spectrum over that N .
B: I use that to normalize the s the current center frame by mean subtraction .
B: And I then then I move to the next frame and I  I do it again .
B: Well , actually I calculate all the means first
B: and then I do the subtraction .
B: And um the I tried that with HDK ,
B: the Aurora setup of HDK training on clean TI - digits ,
B: and um it it helped
B: um in a phony reverberation case
B: um where I just used the simulated impulse response um the error rate went from something like eighty it was from something like eighteen percent to um four percent .
B: And on meeting rec recorder far mike digits , mike on channel F , it went from um  forty - one percent error to eight percent error .
E: On on the real data , not with artificial reverb ?
B: Right .
E: Uh - huh .
B: And that that was um trained on clean speech only ,
B: which I 'm guessing is the reason why the baseline was so bad .
B: And C: That 's ac actually a little side point is I think that 's the first results that we have uh uh uh of any sort on the far field uh on on the far field data uh for recorded in in meetings .
B: Oh um actually um Adam ran the SRI recognizer .
C: Did he ?
C: On the near field , on the ne
B: On the far field also .
B: He did one PZM channel and one PDA channel .
C: Oh did he ?
C: Oh !
C: I didn't recall that .
C: What kind of numbers was he getting with that ?
B: I  I 'm not sure ,
B: I think it was about five percent error for the PZM channel .
C: Five .
B: f I think .
B: Yeah .
C: So why were you getting forty - one here ?
C: Is this B: Um .
B: I I 'm g I 'm guessing it was the the training data .
B: Uh , clean TI - digits is , like , pretty pristine training data ,
B: and if they trained the SRI system on this TV broadcast type stuff ,
B: I think it 's a much wider range of channels
B: and it C: No ,
C: but wait a minute .
C: I I I th I think he C: What am I saying here ?
C: Yeah ,
C: so that was the SRI system .
C: Maybe you 're right .
C: Yeah .
C: Cuz it was getting like one percent  So it 's still this kind of ratio .
C: It was it was getting one percent or something on the near field .
C: Wasn't it ?
E: Mm - hmm ,
E: or it wa a it was around one .
C: Yeah .
C: Yeah . I think it was getting around one percent for the near for the n for the close mike .
E: Yeah .
B: Huh ?
C: So it was like one to five B: OK .
C: So it 's still this kind of ratio .
C: It 's just C: yeah ,
C: it 's a lot more training data .
C: So
C: So probably it should be something we should try then is to is to see if is at some point just to take i to transform the data
C: and then  and then uh use th use it for the SRI system .
B: b You me you mean um ta
C: So you 're so you have a system which for one reason or another is relatively poor ,
B: Yeah .
C: and and uh you have something like forty - one percent error
C: uh and then you transform it to eight by doing doing this this work .
C: Um .
C: So here 's this other system , which is a lot better ,
C: but there 's still this kind of ratio .
C: It 's something like five percent error with the the distant mike ,
C: and one percent with the close mike .
B: OK .
C: So the question is how close to that one can you get if you transform the data using that system .
B: r Right , so so I guess this SRI system is trained on a lot of s Broadcast News or Switchboard data .
C: Yeah .
B: Is that right ?
B: Do you know which one it is ?
E: It 's trained on a lot of different things .
E: Um .
E: It 's trained on uh a lot of Switchboard , Call Home ,
B: Uh - huh .
E: um a bunch of different sources ,
E: some digits ,
E: there 's some digits training in there .
B: OK .
B: O one thing I 'm wondering about is what this mean subtraction method um will do if it 's faced with additive noise .
A: Hmm .
B: Cuz I I it 's cuz I don't know what log magnitude spectral subtraction is gonna do to additive noise .
C: Yeah ,
C: well , it 's it 's not exactly the right thing
B: That 's that 's the B: Uh - huh .
C: but uh but you 've already seen that cuz there is added noise here .
B: That 's that 's Yeah , that 's true .
C: Yeah .
B: That 's a good point .
C: So um B: OK , so it 's then then it 's it 's it 's reasonable to expect it would be helpful if we used it with the SRI system
B: and
C: Yeah ,
C: I mean , as helpful C: I mean ,
C: so that 's the question .
C: Yeah , w we 're often asked this when we work with a system that that isn't isn't sort of industry industry standard great ,
B: Uh - huh .
C: uh and we see some reduction in error using some clever method ,
C: then , you know , will it work on a  on a on a good system .
C: So uh
C: you know , this other one 's it was a pretty good system .
C: I think , you know , one one percent word error rate on digits is uh digit strings is not uh you know stellar ,
C: but but given that this is real digits , as opposed to uh sort of laboratory B: Mm - hmm .
C: Well .
E: And it wasn't trained on this task either .
C: And it wasn't trained on this task .
C: Actually one percent is sort of you know , sort of in a reasonable range .
C: People would say " yeah , I could I can imagine getting that " .
B: Mm - hmm .
C: And uh so the the four or five percent or something is is is quite poor .
B: Mm - hmm .
C: Uh , you know , if you 're doing a uh  a sixteen digit uh credit card number you 'll basically get it wrong almost all the time .
C: So . So . Uh , um
B: Hmm .
C: a significant reduction in the error for that would be great .
B: Huh , OK .
C: And and then , uh
C: Yeah .
C: So .
C: Yeah .
C: Cool .
B: Sounds good .
C: Yeah .
C: Alright , um , I actually have to run .
C: So I don't think I can do the digits ,
C: but um , I guess I 'll leave my microphone on ?
E: Uh , yeah .
C: Yeah .
C: Thank you .
E: Yep .
E: Yeah .
E: That 'll work .
C: I can be out of here quickly . <comment> <comment>  That 's
C: I just have to run for another appointment . OK ,
C: I t Yeah . I left it on .
C: OK .