professor c: so you get to phd d: Yeah , I will try to explain the thing that I did this this week during this week . phd d: Well eh you know that I work I begin to work with a new feature to detect voice - unvoice . phd d: What I trying two MLP to to the with this new feature and the fifteen feature  from the eh bus base system phd e: The the mel cepstrum ? phd d: No , satly the mes the Mel Cepstrum , the new base system the new base system . phd d: And I 'm trying two MLP , one one that only have t three output , voice , unvoice , and silence , professor c:   And I tried to do some experiment of recognition with that and only have result with with the MLP with the three output . professor c: What what feeds the  the three - output net ? phd d: Voice , unvoice , and si professor c: No no , what feeds it ? What features does it see ? phd d: The feature the input ? The inputs are the fifteen the fifteen  bases feature . And the other three features are R , the variance of the difference between the two spectrum , professor c:  - huh . phd d: the variance of the auto - correlation function , except the the first point , because half the height value is R - zero professor c:   professor c: You wouldn't do like R - one over R - zero or something like that ?  usually for voiced - unvoiced you 'd do yeah , you 'd do something you 'd do energy phd d: Yeah . professor c: but then you have something like spectral slope , which is you get like R - one ov over R - zero or something like that . phd d: Auto - correlation ? Yes , yes , the variance of the auto - correlation function that uses that professor c: Ye - Well that 's the variance , but if you just say " what is "  , to first order ,  yeah one of the differences between voiced , unvoiced and silence is energy . phd d: Yeah , I I 'll The spectral shape , professor c: Yeah , and so R - one over R - zero is what you typically use for that . No , I don't use that I can't use professor c: No , I 'm saying that 's what people us typically use . professor c: See , because it because this is this is just like a single number to tell you  " does the spectrum look like that or does it look like that " . professor c: So if it 's if it 's  if it 's low energy  but the but the spectrum looks like that or like that , it 's probably silence . professor c:  but if it 's low energy and the spectrum looks like that , it 's probably unvoiced . professor c: So if you just if you just had to pick two features to determine voiced - unvoiced , you 'd pick something about the spectrum like  R - one over R - zero ,  and R - zero phd d:  -  , OK . professor c: or i i you know you 'd have some other energy measure and like in the old days people did like  zero crossing counts .  , phd d: Bec - because the result are a little bit better but we have in a point that everything is more or less the similar more or less similar . professor c: Right , but it seemed to me that what you were what you were getting at before was that there is something about the difference between the original signal or the original FFT and with the filter which is what and the variance was one take  on it . Then in that case , if you have two nets , Alright , and this one has three outputs , and this one has f phd d:   professor c: if you were to sum up the probabilities for the voiced and for the unvoiced and for the silence here , we 've found in the past you 'll do better at voiced - unvoiced - silence than you do with this one . professor c: So  phd d: No phd e: So you 're saying take the features that go into the voiced - unvoiced - silence net and feed those into the other one , as additional inputs , rather than having a separate professor c: w W well that 's another way . professor c: That wasn't what I was saying but yeah that 's certainly another thing to do . No I was just trying to say if you b if you bring this into the picture over this , what more does it buy you ? phd e: Mmm . professor c: And what I was saying is that the only thing I think that it buys you is  based on whether you feed it something different . And so the kind of thing that that she was talking about before , was looking at something  ab  something  about the difference between the the   log FFT  log power  and the log magnitude  F F - spectrum  and the   filter bank . professor c: And so the filter bank is chosen in fact to sort of integrate out the effects of pitch and she 's saying you know trying So the particular measure that she chose was the variance of this m of this difference , but that might not be the right number . professor c: Right ?  maybe there 's something about the variance that 's that 's not enough or maybe there 's something else that that one could use , but I think that , for me , the thing that that struck me was that  you wanna get something back here , so here 's here 's an idea .  What about it you skip all the all the really clever things , and just fed the log magnitude spectrum into this ? phd d: Ah I 'm sorry . professor c: This is f You have the log magnitude spectrum , and you were looking at that and the difference between the filter bank and and c c computing the variance . professor c: What if you stopped being clever ? And you just took this thing in here because it 's a neural net and neural nets are wonderful phd d:   professor c: and figure out what they can what they most need from things , and  that 's what they 're good at . professor c: So  you 're you 're you 're trying to be clever and say what 's the statistic that should we should get about this difference but  in fact , you know maybe just feeding this in or or feeding both of them in phd e:  . professor c: you know , another way , saying let it figure out what 's the what is the interaction , especially if you do this over multiple frames ? phd d:   professor c: Then you have this over time , and and both kinds of measures and  you might get  something better . phd e: So so don't  don't do the division , but let the net have everything .  , it seems to me , if you have exactly the right thing then it 's better to do it without the net because otherwise you 're asking the net to learn this you know , say if you wanted to learn how to do multiplication . professor c:  you could feed it a bunch of s you could feed two numbers that you wanted to multiply into a net and have a bunch of nonlinearities in the middle and train it to get the product of the output and it would work . But , it 's kind of crazy , cuz we know how to multiply and you you 'd be you know much lower error usually if you just multiplied it out . phd e: How long does it take , Carmen , to train up one of these nets ? phd d: Oh , not too much . grad a: What are what are your f  frame error rates for for this ? phd d: Eh fifty - f six  no , the frame error rate ? grad a: O phd d: Fifty - six I think . grad a: Fif - fifty - six percent accurate for v voice - unvoice phd d: The accuracy . No for , yes f I don't remember for voice - unvoice , grad a: Oh , OK . phd d: But I think that fifty - five was for the when the output are the fifty - six phone . phd d: That I look in the with the other nnn the other MLP that we have are more or less the same number . professor c: I think at the frame level for fifty - six that was the kind of number we were getting for for   reduced band width  stuff . phd d: I think that I I I think that for the other one , for the three output , is sixty sixty - two , sixty three more or less .  , if you 're getting fifty - six percent over here ,  that 's in noise also , right ? phd d: Yeah , yeah , yeah . If you 're getting fifty - six here , try adding together the probabilities of all of the voiced phones here and all of the unvoiced phones phd d: will be professor c: and see what you get then . phd d: Well I don't know , but I th I I think that we I have the result more or less . I don't I 'm not sure but I remember @ @ that I can't show that . professor c: OK , but that 's a That is a a good check point , you should do that anyway , phd d: Yeah . professor c: OK ? Given this this  regular old net that 's just for choosing for other purposes ,  add up the probabilities of the different subclasses and see see how well you do . But phd e: The targets for the neural net , they come from forced alignments ? phd d: no . Well there 's gonna be it looks like there 's gonna be a noisy  some large vocabulary noisy stuff too . phd e: Oh ! professor c: Yeah , so the   , the issue is whether people make a decision now based on what they 've already seen , or they make it later . And one of the arguments for making it later is let 's make sure that whatever techniques that we 're using work for something more than than connected digits . phd e: When are they planning When would they do that ? professor c: Mmm , I think late  I think in the summer sometime . phd d: and also mmm I H Hynek last week say that if I have time I can to begin to to study well seriously the France Telecom proposal professor c:   phd d: to look at the code and something like that to know exactly what they are doing because maybe that we can have some ideas professor c:   Look insi look i carefully what they are doing with the program @ @ and I begin to to work also in that . But the first thing that I don't understand is that they are using R - the  log energy that this quite I don't know why they have some constant in the expression of the lower energy . professor c: Oh , at the front it says  " log energy is equal to the rounded version of sixteen over the log of two " phd d: This Yeah . professor c: Well , this is natural log , and maybe it has something to do with the fact that this is I I have no idea . phd e: Is that some kind of base conversion , or ? professor c: Yeah , that 's what I was thinking , but but  , then there 's the sixty - four , I don't know . phd d: Because maybe they 're the threshold that they are using on the basis of this value phd e: Experimental results . phd d: I don't know exactly , because well th I thought maybe they have a meaning . phd e: So they 're taking the number inside the log and raising it to sixteen over log base two . If we ignore the sixteen , the natural log of t one over the natural log of two times the natu I don't know . Well , maybe somebody 'll think of something , phd e:  professor c: but this is  It may just be that they they want to have for very small energies , they want to have some kind of a phd d: Yeah , the e The effect I don't @ @ I can understand the effect of this , no ? because it 's to to do something like that . professor c: Well , it says , since you 're taking a natural log , it says that when when you get down to essentially zero energy , this is gonna be the natural log of one , which is zero . professor c: So it 'll go down to  to the natural log being So the lowest value for this would be zero . I I will look to try if I move this parameter in their code what happens , maybe everything is Maybe they tres hole are on basis of this . professor c:   it they they probably have some fi particular s fixed point arithmetic that they 're using , phd d: I don't know . professor c: and then it just phd e: Yeah , I was just gonna say maybe it has something to do with hardware , professor c: Yeah . I think you 're supposed to on this stuff anyway , and and so maybe that puts it in the right realm somewhere . phd e: Well it just , yeah , puts it in the right range , or professor c: Yeah . I think , given at the level you 're doing things in floating point on the computer , I don't think it matters , would be my guess , phd d:   OK , and wh when did Stephane take off ? He took off phd d: I think that Stephane will arrive today or tomorrow . professor c: Oh , he was gone these first few days , and then he 's here for a couple days before he goes to Salt Lake City . I I don't know if there are many people who are going to ICASSP phd d: Yeah . phd e: Do have Have people sort of stopped going to ICASSP in recent years ? professor c: people are less consistent about going to ICASSP and I think it 's still it 's still a reasonable forum for students to to present things .  , it 's I think for engineering students of any kind , I think it 's it 's if you haven't been there much , it 's good to go to ,  to get a feel for things , a range of things , not just speech . But I think for for sort of dyed - in - the - wool speech people ,  I think that ICSLP and Eurospeech are much more targeted . And then there 's these other meetings , like HLT and and  ASRU phd e:  professor c: so there 's there 's actually plenty of meetings that are really relevant to to  computational  speech processing of one sort or another . So  Wanna talk a little bit about what we were talking about this morning ? grad a: Oh !   Yeah . I I guess some of the progress , I I 've been getting a getting my committee members for the quals . And  so far I have Morgan and Hynek , Mike Jordan , and I asked John Ohala and he agreed . Then  I talked a little bit about  continuing with these dynamic ev  acoustic events , and  we 're we 're we 're thinking about a way to test the completeness of a a set of  dynamic  events .  , completeness in the in the sense that  if we if we pick these X number of acoustic events , do they provide sufficient coverage for the phones that we 're trying to recognize or or the f the words that we 're gonna try to recognize later on . And so Morgan and I were  discussing  s  s a form of a cheating experiment where we get  we have   a chosen set of features , or acoustic events , and we train up a hybrid  system to do phone recognition on TIMIT . So i i the idea is if we get good phone recognition results , using  these set of acoustic events , then  that that says that these acoustic events are g sufficient to cover a set of phones , at least found in TIMIT .  so i it would be a a measure of " are we on the right track with with the the choices of our acoustic events " . And also , just  working on my  final project for Jordan 's class ,  which is professor c: Actually , let me grad a: Yeah . The the other thing I was suggesting , though , is that given that you 're talking about binary features , maybe the first thing to do is just to count and  count co - occurrences and get probabilities for a discrete  cuz that 'd be pretty simple because it 's just Say , if you had ten ten events ,  that you were counting ,  each frame would only have a thousand possible values for these ten bits , and  so you could make a table that would say , if you had thirty - nine phone categories , that would be a thousand by thirty - nine , and just count the co - occurrences and divide them by the the    occ  count the co - occurrences between the event and the phone and divide them by the number of occurrences of the phone , and that would give you the likelihood of the of the event given the phone . And  then just use that in a very simple  and  you could  do phone recognition then and  wouldn't have any of the issues of the  training of the net or  , it 'd be on the simple side , but phd e:   professor c:   you know , if   the example I was giving was that if if you had  onset of voicing and and end of voicing as being two kinds of events , then if you had those a all marked correctly , and you counted co - occurrences , you should get it completely right . So   If you just do this by counting , then you should be able to find out in a pretty straightforward way whether you have a sufficient  set of events to to do the kind of level of of  classification of phones that you 'd like . And then the other thing that we were discussing was was  OK , how do you get the your training data . professor c: Cuz  the Switchboard transcription project   you know was half a dozen people , or so working off and on over a couple years , and  similar similar amount of data to what you 're talking about with TIMIT training . So , it seems to me that the only reasonable starting point is  to automatically translate the  current TIMIT markings into the markings you want . And  it won't have the kind of characteristic that you 'd like , of catching funny kind of things that maybe aren't there from these automatic markings , phd e:   professor c: but but  it 's  phd e: It 's probably a good place to start . professor c: Yeah and a short short amount of time , just to again , just to see if that information is sufficient to  determine the phones . phd e: Yeah , you could even then to to get an idea about how different it is , you could maybe take some subset and you know , go through a few sentences , mark them by hand and then see how different it is from you know , the canonical ones , professor c: Right . phd e: just to get an idea a rough idea of h if it really even makes a difference . professor c:   my my guess would be that this is since TIMIT 's read speech that this would be less of a big deal , phd e:   professor c: And the other thing would be , say , if you had these ten events , you 'd wanna see , well what if you took two events or four events or ten events or t and you know , and and hopefully there should be some point at which having more information doesn't tell you really all that much more about what the phones are . professor c: you could , but the thing is , what he 's talking about here is a  a translation to a per - frame feature vector , so there 's no sequence in that , I think . I think it 's just a phd e: Unless you did like a second pass over it or something after you 've got your professor c: Yeah , but we 're just talking about something simple here , yeah , to see if phd e: Yeah . The idea is with a with a very simple statistical structure , could you could you  at least verify that you 've chosen features that are sufficient . professor c: OK , and you were saying something starting to say something else about your your class project , or ? grad a: Oh . grad a: So for my class project I 'm  I 'm tinkering with  support vector machines ? something that we learned in class , and   basically just another method for doing classification . And so I 'm gonna apply that to  compare it with the results by  King and Taylor who did  these  using recurrent neural nets , they recognized  a set of phonological features  and made a mapping from the MFCC 's to these phonological features , so I 'm gonna do a similar thing with with support vector machines and see if phd e: So what 's the advantage of support vector machines ? What grad a:  . grad a: and  so if you if you give it less data it still does a reasonable job in learning the the patterns . grad a:  and  professor c: I guess it yeah , they 're sort of succinct , and and they  grad a: Yeah . phd e: Does there some kind of a distance metric that they use or how do they for cla what do they do for classification ? grad a:  . So , the the simple idea behind a support vector machine is  , you have you have this feature space , right ? and then it finds the optimal separating plane ,  between these two different  classes , phd e:   grad a: and  and so  , what it i at the end of the day , what it actually does is it picks those examples of the features that are closest to the separating boundary , and remembers those phd e:   So , given these  these features , or or these these examples , critical examples , which they call support f support vectors , then  given a new example , if the new example falls  away from the boundary in one direction then it 's classified as being a part of this particular class phd e: Oh . phd e: So why save the examples ? Why not just save what the boundary itself is ? grad a:   professor c: You know , it it goes back to nearest - neighbor sort of thing , phd e:   professor c: right ?  , i i if is it eh w When is nearest - neighbor good ? Well , nearest - neighbor good is good if you have lots and lots of examples .  but of course if you have lots and lots of examples , then it can take a while to to use nearest - neighbor . So a long time ago people talked about things where you would have  a condensed nearest - neighbor , where you would you would you would pick out  some representative examples which would  be sufficient to represent to to correctly classify everything that came in . professor c: I I think s I think support vector stuff sort of goes back to to that kind of thing . So rather than doing nearest neighbor where you compare to every single one , you just pick a few critical ones , and professor c: Yeah . professor c: And th the You know ,  neural net approach  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 and then you you place them as you best can to define the boundaries , and in fact , as you know , these things do take a lot of parameters and and  if you have  only a modest amount of data , you have trouble  learning them .  , so I I guess the idea to this is that it it is reputed to  be somewhat better in that regard . I it can be a a reduced  parameterization of of the the model by just keeping certain selected examples . professor c: But I don't know if people have done sort of careful comparisons of this on large tasks or anything . grad b: S do you get some kind of number between zero and one at the output ? grad a: Actually you don't get a you don't get a nice number between zero and one .  ,  there are there are pap Well , basically , it 's it 's  you you get a distance measure at the end of the day , and then that distance measure is is  is translated to a zero or one . professor c: But that 's looking at it for for classification for binary classification , grad a: That 's for classification , right . professor c: right ? phd e: And you get that for each class , you get a zero or a one . grad a: You have the distances to work with , professor c: Cuz actually Mississippi State people did use support vector machines for   speech recognition and they were using it to estimate probabilities . Yeah , they they had a had a way to translate the distances into into probabilities with the with the simple   sigmoidal function . professor c: Yeah , and d did they use sigmoid or a softmax type thing ? grad a:  Yeah , professor c: And didn't they like exponentiate or something grad a: there 's some there 's like one over one plus the exponential or something like that . professor c: and then divide by the sum of them , or ? Oh it i Oh , so it is a sigmoidal . phd e: Did the did they get good results with that ? professor c: they 're OK , I I don't I don't think they were earth earth shattering , but I think that  this was a couple years ago , phd e:  . professor c: I remember them doing it at some meeting , and and  I don't think people were very critical because it was interesting just to to try this and you know , it was the first time they tried it , so so the you know , the numbers were not incredibly good phd e:  . grad b:  s So Barry , if you just have zero and ones , how are you doing the speech recognition ? grad a: Oh I 'm not do I 'm not planning on doing speech recognition with it . grad a: So  for example , this this  feature set called the  sound patterns of English  is just a bunch of  binary valued features . Let 's say , is this voicing , or is this not voicing , is this sonorants , not sonorants , and stuff like that . phd e: Did you find any more mistakes in their tables ? grad a: Oh !  I haven't gone through the entire table , yet . Yeah , yesterday I brought Chuck the table and I was like , " wait , this is Is the mapping from N to to this phonological feature called  " coronal " , is is should it be shouldn't it be a one ? or should it should it be you know coronal instead of not coronal as it was labelled in the paper ? " So I ha haven't hunted down all the all the mistakes yet , professor c:  - huh . grad a: but professor c: But a as I was saying , people do get probabilities from these things , grad b: OK . professor c: and and  we were just trying to remember how they do , but people have used it for speech recognition , and they have gotten probabilities . professor c: There 's you have you have the paper , right ? The Mississippi State paper ? grad a:   professor c: Yeah , if you 're interested y you could look , grad b: And OK . grad a: yeah , our phd e: So in your in in the thing that you 're doing ,  you have a vector of ones and zeros for each phone ? grad a:    phd e: Is that what you 're grad a: Right , Right , right f so for every phone there is there is a  a vector of ones and zeros f  corresponding to whether it exhibits a particular phonological feature or not . And so when you do your wh I 'm what is the task for the class project ? To come up with the phones ? grad a:  phd e: or to come up with these vectors to see how closely they match the phones , grad a: Oh . Right ,  to come up with a mapping from  MFCC 's or s some feature set ,  to  w to whether there 's existence of a particular phonological feature . grad a: And  yeah , basically it 's to learn a mapping from from the MFCC 's to  phonological features . C phd e: I guess  ,  I 'm not sure what you what you 're what you get out of your system . Do you get out a  a vector of these ones and zeros and then try to find the closest matching phoneme to that vector , grad a:   Just it 's it 's basically it 's it 's really simple , basically a detection of phonological features . grad a: and  cuz the  So King and and Taylor  did this with  recurrent neural nets , phd e: Yeah . grad a: and this i their their idea was to first find a mapping from MFCC 's to  phonological features phd e:   grad a: and then later on , once you have these phonological features , then  map that to phones . grad a: So I 'm I 'm sort of reproducing phase one of their stuff . I wo did they compare that  , what if you just did phone recognition and did the reverse lookup . phd e: So you recognize a phone and which ever phone was recognized , you spit out it 's vector of ones and zeros . phd e:   professor c: That 's probably not what he 's going to do on his class project . professor c: So  have you had a chance to do this  thing we talked about yet with the   phd e: Insertion penalty ? professor c:  . No actually I was going a different That 's a good question , too , but I was gonna ask about the the  changes to the data in comparing PLP and mel cepstrum for the SRI system . 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 . And I was asking if you were going to do redo it  for PLP with the normalization done as it had been done for the mel cepstrum . phd e: What I 've been doing is  trying to figure out it just seems to me like there 's a  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 . phd e: So what I was working on is  just going through and checking the headers of the wavefiles , to see if maybe there was a  a certain type of compression or something that was done that my script wasn't catching . phd e: Which would you know cause it to perform OK , but  , 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 . 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 . So I was doing that first , before I did these other things , just to make sure there wasn't something professor c: Although really , a couple three percent  difference in word error rate  could easily come from some difference in normalization , I would think . But phd e: Yeah , and I think , hhh I 'm trying to remember but I think I recall that Andreas was saying that he was gonna run sort of the reverse experiment .  which is to try to emulate the normalization that we did but with the mel cepstral features . professor c: Yeah , he 's probably off at at  his meeting now , phd e: Yeah , he 's gone now . But yeah phd e: But professor c: the I sh think they should be roughly equivalent ,   again the Cambridge folk found the PLP actually to be a little better . professor c:  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  I was looking I 've been studying and going through the logs for the system that  Andreas created . And  his  the way that the S R I system looks like it works is that it reads the wavefiles directly ,  and does all of the cepstral computation stuff on the fly . 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 . phd e: But it looked like he had to give it  even though the cepstrum is already computed , he has to give it  a front - end parameter file . Which talks about the kind of  com computation that his mel cepstrum thing does , professor c:  - 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 . I 've been  , I 've been working with  Jeremy on his project and then I 've been trying to track down this bug in  the ICSI front - end features . phd e: So one thing that I did notice , yesterday I was studying the  the  RASTA code professor c:  - huh . phd e: and it looks like we don't have any way to  control the frequency range that we use in our analysis . We basically it looks to me like we do the FFT ,  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: Yeah , I don't think it 's in there , I think it 's in the    the filters . So , the F F T is on everything , but the filters  , 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:  The filter bank which is created by integrating over F F T bins . Yeah , it 's bark scale , and it 's it it  it actually copies the   the second filters over to the first . So the first filters are always and you can s you can specify a different number of  features different number of filters , I think , as I recall . So you can specify a different number of filters , and whatever   you specify , the last ones are gonna be ignored . Y you can't do it without I think changing the number of filters , but phd e: I saw something about  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 highest frequencies are kind of junk . professor c: And so  you just for continuity you just approximate them by by the second to highest and second to lowest . professor c: And and so if you h phd e: But so the but that 's a fixed  thing ? professor c: Yeah , 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 . professor c: And if you had if you had fifty filters , you 'd be throwing away hardly anything . professor c: I don't remember there being an independent way of saying " we 're just gonna make them from here to here " . 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 and thing like that . And I didn't I couldn't see any wh place where that kind of thing was done . But  I didn't quite understand everything that I saw , professor c: Yeah , see I don't know Feacalc at all . 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 . 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  is there a I was wondering if there 's maybe  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 . professor c:  what you can do is  you can definitely change the the filter bank from being  a  trapezoidal integration to a a a triangular one , phd e:   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 , 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 .  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  , I think , is that the the smoothing at the end is auto - regressive instead of being cepstral  , from cepstral truncation . professor c: and that 's that 's why when people started getting databases that had a little more noise in it , like like   Broadcast News and so on , that 's why c Cambridge switched to PLP I think . professor c: So  That 's a difference that I don't think we put any way to get around , since it was an advantage . professor c: but we did eh we did hear this comment from people at some point , that  it  they got some better results with the triangular filters rather than the trapezoidal . 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 . 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  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 . phd e: Yeah , professor c: I would think that you might wanna do something like  you know , look at a few points to see where you are starting to get significant search errors . Well , what I was gonna do is I was gonna take  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 . But  you could  if if if that looks promising you could , you know , r  run the overall test set with a with a few different  pruning thresholds for both , phd e: So . professor c: and presumably he 's running at some pruning threshold that 's that 's  , you know gets very few search errors phd e: Right .  , 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 . And the  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 . 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 . professor c:  Wanna grad b: That 's as far as my stuff goes , professor c: What 's what 's on grad b: yeah , well I tried this mean subtraction method . Due to Avendano , I 'm taking s  six seconds of speech ,  I 'm using two second FFT analysis frames , stepped by a half second so it 's a quarter length step and I I take that frame and four f the four I take Sorry , I take the current frame and the four past frames and the four future frames and that adds up to six seconds of speech . And  the I tried that with HDK , the Aurora setup of HDK training on clean TI - digits , and  it it helped  in a phony reverberation case  where I just used the simulated impulse response  the error rate went from something like eighty it was from something like eighteen percent to  four percent . And on meeting rec recorder far mike digits , mike on channel F , it went from  forty - one percent error to eight percent error . phd e: On on the real data , not with artificial reverb ? grad b: Right . grad b: And that that was  trained on clean speech only , which I 'm guessing is the reason why the baseline was so bad . And professor c: That 's ac actually a little side point is I think that 's the first results that we have    of any sort on the far field  on on the far field data  for recorded in in meetings . professor c: Did he ? On the near field , on the ne grad b: On the far field also . What kind of numbers was he getting with that ? grad b: I I 'm not sure , I think it was about five percent error for the PZM channel .  , clean TI - digits is , like , pretty pristine training data , and if they trained the SRI system on this TV broadcast type stuff , I think it 's a much wider range of channels and it professor c: No , but wait a minute . I I I th I think he What am I saying here ? Yeah , so that was the SRI system . So 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 and then and then  use th use it for the SRI system . grad b: b You me you mean  ta professor c: So you 're so you have a system which for one reason or another is relatively poor , grad b: Yeah . professor c: and and  you have something like forty - one percent error  and then you transform it to eight by doing doing this this work . So here 's this other system , which is a lot better , but there 's still this kind of ratio . It 's something like five percent error with the the distant mike , and one percent with the close mike . professor c: So the question is how close to that one can you get if you transform the data using that system . grad b: r Right , so so I guess this SRI system is trained on a lot of s Broadcast News or Switchboard data . grad b: Do you know which one it is ? phd e: It 's trained on a lot of different things . It 's trained on  a lot of Switchboard , Call Home , grad b:  - huh . phd e:  a bunch of different sources , some digits , there 's some digits training in there . grad b: O one thing I 'm wondering about is what this mean subtraction method  will do if it 's faced with additive noise . Cuz I I it 's cuz I don't know what log magnitude spectral subtraction is gonna do to additive noise . professor c: Yeah , grad b: That 's that 's the professor c: well , it 's it 's not exactly the right thing grad b:  - huh . So  grad 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 and professor c: Yeah , as helpful  , so that 's the question . 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 , grad b:  - huh . professor c:  and we see some reduction in error using some clever method , then , you know , will it work on a on a on a good system . I think , you know , one one percent word error rate on digits is  digit strings is not  you know stellar , but but given that this is real digits , as opposed to  sort of laboratory grad b:   professor c: you know , if you 're doing a  a sixteen digit  credit card number you 'll basically get it wrong almost all the time . So I don't think I can do the digits , but  , I guess I 'll leave my microphone on ? phd e: yeah