OK , so , you 've got some , Xerox things to pass out ? phd a: Yeah , professor d: That are phd a:  .  , lowering the word hour rate is fine , but having big font ! phd a: Next time we will put colors or something . phd a: OK , s so there is kind of summary of what has been done professor d:  Go ahead . So since last week we 've started to fill the column with   features w with nets trained on PLP with on - line normalization but with delta also , because the column was not completely professor d:    phd a: well , it 's still not completely filled , professor d:  phd a: but we have more results to compare with network using without PLP and finally , hhh ,  ehhh PL -  delta seems very important . If you take  , let 's say , anyway Aurora - two - B , so , the next t the second , part of the table , professor d:   phd a:  when we use the large training set using French , Spanish , and English , you have one hundred and six without delta and eighty - nine with the delta . professor d: a And again all of these numbers are with a hundred percent being , the baseline performance , phd a: Yeah , on the baseline , yeah . So professor d: but with a mel cepstra system going straight into the HTK ? phd a: Yeah . So now we see that the gap between the different training set is much   much smaller professor d: Yes . phd a: But , actually , for English training on TIMIT is still better than the other languages . If you take the second set of experiment for Italian , so , the mismatched condition , professor d:   phd a:  when we use the training on TIMIT so , it 's multi - English , we have a ninety - one number , professor d:   phd a: And , yeah , and here the gap is still more important between using delta and not using delta . If y if I take the training s the large training set , it 's we have one hundred and seventy - two , professor d: Yes . Yeah , so the second point is that we have no single cross - language experiments , that we did not have last week .  , so this is training the net on French only , or on English only , and testing on Italian . phd a: And training the net on French only and Spanish only and testing on ,  TI - digits . What we see is that these nets are not as good , except for the multi - English , which is always one of the best . Yeah , then we started to work on a large dat database containing , sentences from the French , from the Spanish , from the TIMIT , from SPINE ,  from  English digits , and from Italian digits . phd a: and  , actually we did this before knowing the result of all the data , so we have to to redo the  the experiment training the net with ,  PLP , but with delta . Well , it 's it 's better than the net using French , Spanish , and English only . The first one , yeah , is combining , two feature streams ,  using and each feature stream has its own MPL . And the third one is to u use a single KLT trans transform features as well as MLP outputs . You know you can you can comment these results , phd b: Yes , I can s I would like to say that , for example , mmm , if we doesn't use the delta - delta ,  we have an improve when we use s some combination . But when phd a: Yeah , we ju just to be clear , the numbers here are  recognition accuracy . phd b: w Yeah , this Yeah , this number recognition acc phd a: So it 's not the Again we switch to another phd b: Yes , and the baseline the baseline have i is eighty - two . phd b: Yeah phd a: So it 's experiment only on the Italian mismatched for the moment for this . phd b: And first in the experiment - one I I do I I use different MLP , professor d:   And I try to combine different type of feature , but the result is that the MSG - three feature doesn't work for the Italian database because never help to increase the accuracy . phd a: Yeah , eh , actually , if w we look at the table , the huge table , we see that for TI - digits MSG perform as well as the PLP , professor d:   phd a: but this is not the case for Italian what where the error rate is c is almost  twice the error rate of PLP . phd a: So , well , I don't think this is a bug but this this is something in probably in the MSG  process that  I don't know what exactly . Perhaps the fact that the the there 's no low - pass filter , well , or no pre - emp pre - emphasis filter and that there is some DC offset in the Italian , or , well , something simple like that . But that we need to sort out if want to  get improvement by combining PLP and MSG professor d:   phd a: And as Carmen said , if we combine the two , we have the result , basically , of PLP . professor d: I  , the  , baseline system when you said the baseline system was  ,  eighty - two percent , that was trained on what and tested on what ? That was ,  Italian mismatched d  , digits , is the testing , phd b: Yeah . professor d: So the " mismatch " just refers to the noise and and ,  microphone and so forth , phd a: Yeah . professor d: right ? So ,  did we have So would that then correspond to the first line here of where the training is is the  Italian digits ? phd b: The train the training of the HTK ? professor d: The phd b: Yes . So ,  So what that says is that in a matched condition , we end up with a fair amount worse putting in the  PLP . Now w would do we have a number , I suppose for the matched I I don't mean matched , but  use of Italian training in Italian digits for PLP only ? phd b:  yes ? phd a:  yeah , so this is basically this is in the table . phd a: Fift - So No , it 's it 's the phd b: No . professor d: No , fifty - two percent of eighty - two ? phd a: Of of of  eighteen phd b: Eighty . So  professor d: Oh this is accuracy !  phd a: so we have nine nine let 's say ninety percent . professor d: OK , so even just PLP , it is not , in the matched condition  I wonder if it 's a difference between PLP and mel cepstra , or whether it 's that the net half , for some reason , is not helping . It 's not Do you have this result with PLP alone , j fee feeding HTK ? professor d: So , s phd a: That That 's what you mean ? phd b: Yeah , phd a: Just PLP at the input of HTK . professor d: so adding MSG phd a:  professor d:  Well , but that 's yeah , that 's without the neural net , phd a: Yeah , that 's without the neural net professor d: right ? phd a: and that 's the result basically that OGI has also with the MFCC with on - line normalization . phd b:  phd a: Eighty - two is the it 's the Aurora baseline , so MFCC . Then we can use well , OGI , they use MFCC th the baseline MFCC plus on - line normalization professor d: Oh , I 'm sorry , I k I keep getting confused because this is accuracy . phd a: So what happ what happens is that when we apply on - line normalization we jump to almost ninety percent . If we use n neural network , even if the features are not correctly normalized , we jump to ninety percent . So professor d: So we go from eighty - si eighty - eight point six to to ninety , or something . phd a: Well , ninety No , I  ninety It 's around eighty - nine , ninety , eighty - eight . So ,  So actually , the answer for experiments with one is that adding MSG , if you  does not help in that case . professor d: The other ones , we 'd have to look at it , but And the multi - English , does  So if we think of this in error rates , we start off with ,  eighteen percent error rate , roughly . professor d:  and we  almost ,  cut that in half by  putting in the on - line normalization and the neural net . About ,  sixteen percent or something of the error , if we use multi - English instead of the matching condition . OK ? So then you 're assuming multi - English is closer to the kind of thing that you could use since you 're not gonna have matching , data for the  for the new for the other languages and so forth .  , one qu thing is that ,  I think I asked you this before , but I wanna double check . When you say " ME " in these other tests , that 's the multi - English , phd a: That 's it 's a part it 's professor d: but it is not all of the multi - English , right ? It is some piece of part of it . professor d: And the multi - English is how much ? phd b: You have here the information . professor d: Oh , so you used almost all You used two thirds of it , phd a: Yeah . So , it it 's still it hurts you seems to hurt you a fair amount to add in this French and Spanish . grad c: Well Stephane was saying that they weren't hand - labeled , phd a: Yeah , it 's phd b: Yeah . Mmm , with the experiment type - two , I first I tried to to combine , nnn , some feature from the MLP and other feature another feature . phd b: And we s we can first the feature are without delta and delta - delta , and we can see that in the situation , the MSG - three , the same help nothing . phd b: And then I do the same but with the delta and delta - delta PLP delta and delta - delta . And they all p but they all put off the MLP is it without delta and delta - delta . And we have a l little bit less result than the the the baseline PLP with delta and delta - delta . phd b: Maybe if when we have the new the new neural network trained with PLP delta and delta - delta , maybe the final result must be better . phd a: Actually , just to be some more phd b:  phd a: Do This number , this eighty - seven point one number , has to be compared with the professor d: Yes , yeah ,  it can't be compared with the other phd a: Which number ? professor d: cuz this is ,  with multi - English , training . professor d: So you have to compare it with the one over that you 've got in a box , which is that ,  the eighty - four point six . professor d: So phd a: Yeah , but  in this case for the eighty - seven point one we used MLP outputs for the PLP net professor d: Yeah . phd b: No , but they they feature @ @ without phd a: So we use feature out  , net outputs together with features . So yeah , this is not perhaps not clear here but in this table , the first column is for MLP and the second for the features . So you 're saying w so asking the question , " What what has adding the MLP done to improve over the , phd a: So , just Yeah so , actually it it it decreased the the accuracy . phd a: And even the MLP alone What gives the MLP alone ? Multi - English PLP . So we have our eighty - three point six and now eighty - eighty point six , phd b: But phd a: that gives eighty - seven point one . Eighty - s I thought it was eighty Oh , OK , eighty - three point six and eighty eighty - eight point six . But I don't know but maybe if we have the neural network trained with the PLP delta and delta - delta , maybe tha this can help . professor d: Well , that 's that 's one thing , but see the other thing is that ,  it 's good to take the difficult case , but let 's let 's consider what that means . What what we 're saying is that one o one of the things that  my interpretation of your your s original suggestion is something like this , as motivation . When we train on data that is in one sense or another , similar to the testing data , then we get a win by having discriminant training . professor d: When we train on something that 's quite different , we have a potential to have some problems . professor d: And , if we get something that helps us when it 's somewhat similar , and doesn't hurt us too much when it when it 's quite different , that 's maybe not so bad . professor d: So the question is , if you took the same combination , and you tried it out on ,  on say digits , phd a: On TI - digits ? OK .  , then does that , eh you know maybe with similar noise conditions and so forth , does it does it then look much better ? phd a:   professor d: And so what is the range over these different kinds of  of tests ? So , an anyway . phd b: And , with this type of configuration which I do on experiment using the new neural net with name broad klatt s twenty - seven , d I have found more or less the same result . phd a: So , it 's slightly better , phd b: Little bit better ? phd a: yeah . professor d: And and you know again maybe if you use the , delta there , you would bring it up to where it was ,  you know at least about the same for a difficult case . phd a: It 's either less information from the neural network if we use only the silence output . And then w with the first configuration , I f I am found that work , doesn't work professor d: Yeah . Because I for the del Engli - PLP delta and delta - delta , here I have eighty - five point three accuracy , and with the second configuration I have eighty - seven point one . professor d: by the way , there is a another , suggestion that would apply , to the second configuration , which , was made , by , Hari . And that was that , if you have  feed two streams into HTK , and you , change the ,  variances if you scale the variances associated with ,  these streams  , you can effectively scale the streams . Right ? So , you know , without changing the scripts for HTK , which is the rule here , you can still change the variances phd a:   professor d: And , so , if you do that , for instance it may be the case that , the MLP should not be considered as strongly , for instance . professor d: And , so this is just setting them to be , excuse me , of equal equal weight . professor d: Right ? You know , I I 'm sorry to say that gives more experiments if we wanted to look at that , but but , you know on the other hand it 's just experiments at the level of the HTK recognition . Yeah , you have to change the No , you can just do it in as once you 've done the training grad c: And then you can vary it . professor d: Yeah , the training is just coming up with the variances so I guess you could you could just scale them all . But Is it i th  the HTK models are diagonal covariances , so I d Is it professor d: That 's  , exactly the point , I think , that if you change  , change what they are phd a:  . professor d: It 's diagonal covariance matrices , but you say what those variances are . professor d: So , that you know , it 's diagonal , but the diagonal means th that then you 're gonna it 's gonna it 's gonna internally multiply it and and  , i it im  implicitly exponentiated to get probabilities , and so it 's it 's gonna it 's it 's going to affect the range of things if you change the change the variances of some of the features . phd b: do ? professor d: So , i it 's precisely given that model you can very simply affect , the s the strength that you apply the features . So it could just be that h treating them equally , tea treating two streams equally is just just not the right thing to do . Of course it 's potentially opening a can of worms because , you know , maybe it should be a different number for for each kind of test set , or something , phd a:   professor d: So I guess the other thing is to take you know if one were to take , you know , a couple of the most successful of these , phd a: Yeah , and test across everything . phd a: So , the next point , yeah , we 've had some discussion with Steve and Shawn , about their  , articulatory stuff ,  . phd a: discussion with Hynek , Sunil and Pratibha for trying to plug in their our our networks with their within their block diagram , where to plug in the the network , after the the feature , before as  a as a plugin or as a anoth another path , discussion about multi - band and TRAPS , actually Hynek would like to see , perhaps if you remember the block diagram there is , temporal LDA followed b by a spectral LDA for each  critical band . And he would like to replace these by a network which would , make the system look like a TRAP . Basically , this is a TRAP system kind of TRAP system , but where the neural network are replaced by LDA .  , yeah , and about multi - band , I started multi - band MLP trainings ,  mmh Actually , I w I w hhh prefer to do exactly what I did when I was in Belgium . So I take exactly the same configurations , seven bands with nine frames of context , and we just train on TIMIT , and on the large database , so , with SPINE and everything . So , this would would be something between TRAPS and multi - band because we still have quite large bands , and but with a lot of context also . So  Yeah , we still have to work on Finnish , basically , to make a decision on which MLP can be the best across the different languages .  , well , the next part of the document is , well , basically , a kind of summary of what everything that has been done . We have seventy - nine M L Ps trained on one , two , three , four , three , four , five , six , seven ten on ten different databases . phd a: the number of frames is bad also , so we have one million and a half for some , three million for other , and six million for the last one .  , yeah ! As we mentioned , TIMIT is the only that 's hand - labeled , and perhaps this is what makes the difference . First ,  with respect to the on - line normalization , there are that use bad on - line normalization , and other good on - line normalization . With respect to the features , with respect to the use of delta or no ,  with respect to the hidden layer size and to the targets . s What 's this ? We only have two hundred eighty six different tests And no not two thousand . phd b: I say this morning that @ @ thought it was the professor d: Alright , now I 'm just slightly impressed , OK . but when the M - MLP is trained on the  is not trained on the target task , it increased the error rate compared to using straight features . Except if the features are bad  , actually except if the features are not correctly on - line normalized . In this case the tandem is still better even if it 's trained on not on the target digits . phd a: so the fourth point is , yeah , the TIMIT plus noise seems to be the training set that gives better the best network . professor d: So , on the MSG  problem  , I think that in in the  , in the short time solution  , that is , trying to figure out what we can proceed forward with to make the greatest progress , phd a:   professor d: much as I said with JRASTA , even though I really like JRASTA and I really like MSG , phd a:   professor d: I think it 's kind of in category that it 's , it it may be complicated . professor d: And  it might be if someone 's interested in it , certainly encourage anybody to look into it in the longer term , once we get out of this particular rush  for results . professor d: But in the short term , unless you have some some s strong idea of what 's wrong , phd a: I don't know at all but I 've perhaps I have the feeling that it 's something that 's quite quite simple or just like nnn , no high - pass filter professor d: Yeah , probably . professor d: There 's supposed to well MSG is supposed to have a an on - line normalization though , right ? phd a: It 's There is , yeah , an AGC - kind of AGC . professor d: Yeah , but also there 's an on - line norm besides the AGC , there 's an on - line normalization that 's supposed to be  , yeah , phd a: Mmm . professor d: In fac in fact the on - line normalization that we 're using came from the MSG design , phd a:  . Are your results are still with the bad the bad phd b: Maybe , may No ? With the better phd a: With the O - OLN - two ? phd b: No ? phd a: Ah yeah , you have you have OLN - two , phd b: Oh ! Yeah , yeah , yeah ! With " two " , with " on - line - two " . professor d: " Two " is good ? phd a: And professor d: No , " two " is bad . It 's probably something simple  , i if if  someone , you know , wants to play with it for a little bit . professor d: but but my my guess would be that it 's something that is a simple thing that could take a while to find . That 's that what we were concerned about is that if it 's not on the target task If it 's on the target task then it it it helps to have the MLP transforming it . professor d: If it  if it 's not on the target task , then , depending on how different it is ,  you can get  , a reduction in performance . professor d: And the question is now how to how to get one and not the other ? Or how to how to ameliorate the the problems . professor d: because it it certainly does is nice to have in there , when it when there is something like the training data . So , the the reason Yeah , the reason is that the perhaps the target the the task dependency the language dependency , and the noise dependency professor d: So that 's what you say th there . phd a: Well , the e e But this is still not clear because , I I I don't think we have enough result to talk about the the language dependency . Well , the TIMIT network is still the best but there is also an the other difference , the fact that it 's it 's hand - labeled .  , I d I don't think we want to mess with the microphones but it 's  Just  , have a seat . s Summary of the first  ,  forty - five minutes is that some stuff work and works , and some stuff doesn't OK , phd a: We still have  this One of these perhaps ? phd b: Yeah .  professor d: Yeah , I guess we can do a little better than that but I think if you if you start off with the other one , actually , that sort of has it in words and then th that has it the associated results . So you 're saying that  , although from what we see , yes there 's what you would expect in terms of a language dependency and a noise dependency . That is , when the neural net is trained on one of those and tested on something different , we don't do as well as in the target thing . But you 're saying that  , it is Although that general thing is observable so far , there 's something you 're not completely convinced about . What what do you mean ? phd a: mmm , that the the fact that s Well , for for TI - digits the TIMIT net is the best ,  which is the English net . But you have two two effects , the effect of changing language and the effect of training on something that 's Viterbi - aligned instead of hand hand - labeled . professor d: Do you think the alignments are bad ?  , have you looked at the alignments at all ? What the Viterbi alignment 's doing ? phd a: Mmm . Because , that is just looking but  ,  It 's not clear to me you necessarily would do so badly from a Viterbi alignment . But , perhaps it 's not really the the alignment that 's bad but the just the ph phoneme string that 's used for the alignment professor d: Aha ! phd a: Mmm .  professor d: The pronunciation models and so forth phd a:  for We It 's single pronunciation ,  professor d: Aha . phd a: so we asked people to listen to the  the sentence and we gave the phoneme string and they kind of correct them .  , the third The third  issue is the noise dependency perhaps but , well , this is not clear yet because all our nets are trained on the same noises and professor d: I thought some of the nets were trained with SPINE and so forth . professor d: OK , yeah , just don't just need more more results there with that @ @ .  , with respect to the network size , there 's one experiment that 's still running and we should have the result today , comparing network with five hundred and one thousand units . We can , we can tell which training set gives the best result , but we don't know exactly why . professor d: " Multi - multi - English " just means " TIMIT " , phd a: Yeah . professor d: It 's sort of , yes it 's mul it 's multi -  - purpose . So Yeah , the training targets actually , the two of the main issues perhaps are still the language dependency and the noise dependency . And perhaps to try to reduce the language dependency , we should focus on finding some other kind of training targets . For moment you use we use phonetic targets but we could also use articulatory targets , soft targets , and perhaps even ,  use networks that doesn't do classification but just regression so  , train to have neural networks that  , professor d:   phd a: does a regression and well , basically com com compute features and noit not , nnn , features without noise . phd a:  professor d: Yeah , that seems like a good thing to do , probably ,  not  again a short - term sort of thing . professor d:  one of the things about that is that  it 's e u the ri I guess the major risk you have there of being is being dependent on very dependent on the kind of noise and and so forth . phd a: So , this is w w i wa wa this is one thing , this this could be could help could help perhaps to reduce language dependency and for the noise part  we could combine this with other approaches , like , well , the Kleinschmidt approach . I think Kleinschmidt was using more than fifty different noises to train his network , phd b: Yeah . phd a: and So this is one approach and the other is multi - band  , that I think is more robust to the noisy changes . phd a: So perhaps , I think something like multi - band trained on a lot of noises with  , features - based targets could could could help . professor d: Yeah , if you i i It 's interesting thought maybe if you just trained up  w yeah , one one fantasy would be you have something like articulatory targets and you have  some reasonable database ,  but then which is  copied over many times with a range of different noises , phd a:   professor d: And  If Cuz what you 're trying to do is come up with a a core , reasonable feature set which is then gonna be used  , by the the   system . The future work is , well , try to connect to the to make to plug in the system to the OGI system . professor d: And I guess , you know , the the the real open question , e u there 's lots of open questions , but one of the core quote " open questions " for that is  , if we take the  you know , the best ones here , maybe not just the best one , but the best few or something You want the most promising group from these other experiments .  , how well do they do over a range of these different tests , not just the Italian ? phd a: Mmm , professor d:  . professor d: y Right ? And then  then see , again , how We know that there 's a mis there 's a  a a loss in performance when the neural net is trained on conditions that are different than than ,  we 're gonna test on , but well , if you look over a range of these different tests  , how well do these different ways of combining the straight features with the MLP features ,  stand up over that range ? phd b:   And  , take let 's say , just take  multi - English cause that works pretty well for the training . How does that How does that compare between the phd a: So all the all the test sets you mean , yeah . professor d: All the different test sets , phd a: And professor d: and for and for the couple different ways that you have of of of combining them . And perhaps doing this for cha changing the variance of the streams and so on getting different scaling phd b:   Yeah , so thi this sh would be more working on the MLP as an additional path instead of an insert to the to their diagram . Perhaps the insert idea is kind of strange because nnn , they they make LDA and then we will again add a network does discriminate anal nnn , that discriminates , professor d: Yeah . It 's a little strange phd a: or ? Mmm ? professor d: but on the other hand they did it before . And because also perhaps we know that the when we have very good features the MLP doesn't help .  , we we wanna get their path running here , right ? If so , we can add this other stuff . professor d: as an additional path right ? phd a: Yeah , the the way we want to do professor d: Cuz they 're doing LDA RASTA . phd a: The d What ? professor d: They 're doing LDA RASTA , phd a: Yeah , the way we want to do it perhaps is to just to get the VAD labels and the final features . professor d: yeah ? phd a: So they will send us the Well , provide us with the feature files , professor d: I see . phd a: and with VAD  , binary labels so that we can  , get our MLP features and filter them with the VAD and then combine them with their f feature stream . First thing of course we 'd wanna do there is to make sure that when we get those labels of final features is that we get the same results as them . You mean Oh , yeah ! Just re re retraining r retraining the HTK ? professor d: Yeah just th w i i Just to make sure that we have we understand properly what things are , our very first thing to do is to is to double check that we get the exact same results as them on HTK . But ,  just for the testing , jus just make sure that we get the same results so we can duplicate it before we add in another phd a: Mmm . Yeah , so fff , LogRASTA , I don't know if we want to We can try networks with LogRASTA filtered features . But professor d: Oh ! You know , the other thing is when you say comb I 'm I 'm sorry , I 'm interrupting . that u  , when you 're talking about combining multiple features ,  Suppose we said , " OK , we 've got these different features and so forth , but PLP seems pretty good . We have different languages , we have different different noises ,  If we have some drastically different conditions and we just train up different M L Ps with them . What what What Mike found , for the reverberation case at least , who knows if it 'll work for these other ones . That is , that yes , if you knew what the reverberation condition was gonna be and you trained for that , then you got the best results . But if you had , say , a heavily - reverberation ca heavy - reverberation case and a no - reverberation case , and then you fed the thing ,  something that was a modest amount of reverberation then you 'd get some result in between the two . So you you think it 's perhaps better to have several M L Yeah but professor d: It works better if what ? phd a: Yea professor d: I see . Well , see , i oc You were doing some something that was So maybe the analogy isn't quite right . Here the problem seems to be is that we don't have a hug a really huge net with a really huge amount of training data . But we have s f for this kind of task , I would think , sort of a modest amount . We have a modest amount of of  training data from a couple different conditions , and then  in yeah , that and the real situation is that there 's enormous variability that we anticipate in the test set in terms of language , and noise type  , and  , channel characteristic , sort of all over the map . And so , I 'm just concerned that we don't really have  , the data to train up  one of the things that we were seeing is that when we added in we still don't have a good explanation for this , but we are seeing that we 're adding in  , a fe few different databases and  the performance is getting worse and  , when we just take one of those databases that 's a pretty good one , it actually is is is is is better . And  that says to me , yes , that , you know , there might be some problems with the pronunciation models that some of the databases we 're adding in or something like that . But one way or another we don't have  , seemingly , the ability to represent , in the neural net of the size that we have , all of the variability that we 're gonna be covering . So that I 'm I 'm I 'm hoping that  , this is another take on the efficiency argument you 're making , which is I 'm hoping that with moderate size neural nets , that  if we if they look at more constrained conditions they they 'll have enough parameters to really represent them .  i i e The  I think it 's true that the OGI folk found that using LDA RASTA , which is a kind of LogRASTA , it 's just that they have the  it 's done in the log domain , as I recall , and it 's it  it 's just that they d it 's trained up , right ? That that  benefitted from on - line normalization . So will it be in our case , where we 're using the neural net ?  they they were not not using the neural net . We 've been sort of ignoring that , haven't we ? phd a: Yeah , so I don't know . professor d: But phd a: But we have to address the problem of CPU and memory we professor d: Yeah , but I li Well , I think My impression You you folks have been looking at this more than me . But my impression was that  , there was a a a a strict constraint on the delay , phd b: Yeah . professor d: but beyond that it was kind of that  using less memory was better , and using less CPU was better . We have to get some reference point to where we Well , what 's a reasonable number ? Perhaps be because if it 's if it 's too large or large or @ @ professor d: well I don't think we 're  completely off the wall .  I think that if we if we have  ,  the ultimate fall back that we could do If we find   we may find that we we 're not really gonna worry about the M L You know , if the MLP ultimately , after all is said and done , doesn't really help then we won't have it in . professor d: If the MLP does , we find , help us enough in some conditions , we might even have more than one MLP . professor d: And it 's  We do the other manipulations that we 're doing before that .  , what what are they What are they gonna be working Do we know what they 're gonna be working on while we take their features , phd a: They 're They 're starting to wor work on some kind of multi - band . Sunil , what was he doing , do you remember ? phd b: Sunil ? phd a: Yeah . He was doing something new or ? phd b: I I don't re I didn't remember . phd a: I think they were also mainly , well , working a little bit of new things , like networks and multi - band , but mainly trying to tune their their system as it is now to just trying to get the best from this this architecture . So I guess the way it would work is that you 'd get There 'd be some point where you say , " OK , this is their version - one " or whatever , and we get these VAD labels and features and so forth for all these test sets from them , phd a:   We have a certain level we try to improve it with this other path and then  , when it gets to be  , January some point  , we say , " OK we we have shown that we can improve this , in this way . So now   what 's your newest version ? " And then maybe they 'll have something that 's better and then we we 'd combine it .  I I I used to work with  folks who were trying to improve a good  ,  system with  with a neural net system and  , it was a common problem that you 'd Oh , and this Actually , this is true not just for neural nets but just for in general if people were working with  , rescoring  , N - best lists or lattices that come came from  , a mainstream recognizer .  , You get something from the the other site at one point and you work really hard on making it better with rescoring . So by the time you have  , improved their score , they have also improved their score phd a: Mmm . professor d: So , I guess at some point we 'll have to phd a: So it 's professor d: I I don't know . I think we 're we 're integrated a little more tightly than happens in a lot of those cases . I think at the moment they they say that they have a better thing we can we e e phd a: Mmm . professor d: What takes all the time here is that th we 're trying so many things , presumably  , in a in a day we could turn around  , taking a new set of things from them and and rescoring it , phd a: Mmm . I think that the most wide open thing is the issues about the  , you know , different trainings . So we we can for we c we can forget combining multiple features and MLG perhaps , professor d: That 's sort of wide open . phd a: or focus more on the targets and on the training data and ? professor d: Yeah , I think for right now  , I th I I really liked MSG . And I think that , you know , one of the things I liked about it is has such different temporal properties . And  , I think that there is ultimately a really good  , potential for , you know , bringing in things with different temporal properties .  , but  , we only have limited time and there 's a lot of other things we have to look at . professor d: And it seems like much more core questions are issues about the training set and the training targets , and fitting in  what we 're doing with what they 're doing , and , you know , with limited time . And then , you know , once we  , having gone through this process and trying many different things , I would imagine that certain things  , come up that you are curious about  , that you 'd not getting to and so when the dust settles from the evaluation  , I think that would time to go back and take whatever intrigued you most , you know , got you most interested  and  and and work with it , you know , for the next round .  , as you can tell from these numbers  , nothing that any of us is gonna do is actually gonna completely solve the problem . professor d: Well I figured that , but That what what what were you involved in in this primarily ? grad c: helping out  , preparing Well , they 've been kind of running all the experiments and stuff and I 've been  ,  w doing some work on the on the preparing all all the data for them to to  , train and to test on . Right now , I 'm I 'm focusing mainly on this final project I 'm working on in Jordan 's class . What 's what 's that ? grad c: I 'm trying to  So there was a paper in ICSLP about  this this multi - band  , belief - net structure . grad c:  basically it was two H M Ms with with a with a dependency arrow between the two H M professor d:  - huh . grad c: And so I wanna try try coupling them instead of t having an arrow that that flows from one sub - band to another sub - band . And  , I 'm just gonna see if if that that better models  ,  asynchrony in any way or  Yeah . OK , so speaking of which , if we don't have anything else that we need You happy with where we are ? phd a: Mmm . professor d: Know know wher know where we 're going ?  phd a: I think so , yeah . grad e: Al - actually I should mention So if  , about the Linux machine " Swede . grad e: And  Dan Ellis I believe knows something about using that machine so phd a: Mmm . grad e: If people are interested in in getting jobs running on that maybe I could help with that . phd a: Yeah , but I don't know if we really need now a lot of machines . professor d: Right ?  there 's there 's some different things that we 're trying to get at now . Yeah , as far as you can tell , you 're actually OK on C - on CPU  , for training and so on ? Yeah . Well , more is always better , but mmm , I don't think we have to train a lot of networks , now that we know We just select what works fine professor d: OK . to work professor d: And we 're OK on And we 're OK on disk ? phd a: and It 's OK , yeah . Alright , so  , since  , we didn't ha get a channel on for you , you don't have to read any digits but the rest of us will . We didn't  I think I won't touch anything cuz I 'm afraid of making the driver crash which it seems to do , pretty easily . OK , so we 'll  I 'll start off the   connect the phd a: My battery is low