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 ,

and one hundred and four when we use delta.


and one hundred and four when we use delta.

Uh.  Even if the contexts used is quite the same ,


Uh.  Even if the contexts used is quite the same ,

because without delta we use seventeenths  seventeen frames. Uh. Yeah , um , so the second point is that we have no single cross - language experiments , uh , that we did not have last week. Uh , so this is training the net on French only , or on English only , and testing on Italian.


because without delta we use seventeenths  seventeen frames. Uh. Yeah , um , so the second point is that we have no single cross - language experiments , uh , that we did not have last week. Uh , so this is training the net on French only , or on English only , and testing on Italian.

And training the net on French only and Spanish only and testing on , uh TI - digits.


And training the net on French only and Spanish only and testing on , uh TI - digits.

And , fff  um , yeah. 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 , uh , sentences from the French , from the Spanish , from the TIMIT , from SPINE , uh from  uh English digits , and from Italian digits. So this is the  another line  another set of lines in the table. Uh , @ @ with SPINE


And , fff  um , yeah. 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 , uh , sentences from the French , from the Spanish , from the TIMIT , from SPINE , uh from  uh English digits , and from Italian digits. So this is the  another line  another set of lines in the table. Uh , @ @ with SPINE

and  uh , actually we did this before knowing the result of all the data , uh , so we have to to redo the uh  the experiment training the net with , uh PLP , but with delta. But


and  uh , actually we did this before knowing the result of all the data , uh , so we have to to redo the uh  the experiment training the net with , uh PLP , but with delta. But

um this  this net performed quite well. Well , it 's  it 's better than the net using French , Spanish , and English only. Uh. So , uh , yeah. We have also started feature combination experiments. Uh many experiments using features and net outputs together. And this is  The results are on the other document. Uh , we can discuss this after , perhaps  well , just , @ @. Yeah , so basically there are four  four kind of systems. The first one , yeah , is combining , um , two feature streams , uh using  and each feature stream has its own MPL. So it 's the  kind of similar to the tandem that was proposed for the first. The multi - stream tandem for the first proposal. The second is using features and KLT transformed MLP outputs. And the third one is to u use a single KLT trans transform features as well as MLP outputs. Um , yeah. Mmm. You know you can  you can comment these results ,


and  uh , actually we did this before knowing the result of all the data , uh , so we have to to redo the uh  the experiment training the net with , uh PLP , but with delta. But

um this  this net performed quite well. Well , it 's  it 's better than the net using French , Spanish , and English only. Uh. So , uh , yeah. We have also started feature combination experiments. Uh many experiments using features and net outputs together. And this is  The results are on the other document. Uh , we can discuss this after , perhaps  well , just , @ @. Yeah , so basically there are four  four kind of systems. The first one , yeah , is combining , um , two feature streams , uh using  and each feature stream has its own MPL. So it 's the  kind of similar to the tandem that was proposed for the first. The multi - stream tandem for the first proposal. The second is using features and KLT transformed MLP outputs. And the third one is to u use a single KLT trans transform features as well as MLP outputs. Um , yeah. Mmm. You know you can  you can comment these results ,

Yes , I can s I would like to say that , for example , um , mmm , if we doesn't use the delta - delta , uh we have an improve when we use s some combination. But when


um this  this net performed quite well. Well , it 's  it 's better than the net using French , Spanish , and English only. Uh. So , uh , yeah. We have also started feature combination experiments. Uh many experiments using features and net outputs together. And this is  The results are on the other document. Uh , we can discuss this after , perhaps  well , just , @ @. Yeah , so basically there are four  four kind of systems. The first one , yeah , is combining , um , two feature streams , uh using  and each feature stream has its own MPL. So it 's the  kind of similar to the tandem that was proposed for the first. The multi - stream tandem for the first proposal. The second is using features and KLT transformed MLP outputs. And the third one is to u use a single KLT trans transform features as well as MLP outputs. Um , yeah. Mmm. You know you can  you can comment these results ,

Yes , I can s I would like to say that , for example , um , mmm , if we doesn't use the delta - delta , uh we have an improve when we use s some combination. But when

Yeah , we ju just to be clear , the numbers here are uh recognition accuracy.


um this  this net performed quite well. Well , it 's  it 's better than the net using French , Spanish , and English only. Uh. So , uh , yeah. We have also started feature combination experiments. Uh many experiments using features and net outputs together. And this is  The results are on the other document. Uh , we can discuss this after , perhaps  well , just , @ @. Yeah , so basically there are four  four kind of systems. The first one , yeah , is combining , um , two feature streams , uh using  and each feature stream has its own MPL. So it 's the  kind of similar to the tandem that was proposed for the first. The multi - stream tandem for the first proposal. The second is using features and KLT transformed MLP outputs. And the third one is to u use a single KLT trans transform features as well as MLP outputs. Um , yeah. Mmm. You know you can  you can comment these results ,

Yes , I can s I would like to say that , for example , um , mmm , if we doesn't use the delta - delta , uh we have an improve when we use s some combination. But when

Yeah , we ju just to be clear , the numbers here are uh recognition accuracy.

w Yeah , this  Yeah , this number recognition acc


Yes , I can s I would like to say that , for example , um , mmm , if we doesn't use the delta - delta , uh we have an improve when we use s some combination. But when

Yeah , we ju just to be clear , the numbers here are uh recognition accuracy.

w Yeah , this  Yeah , this number recognition acc

So it 's not the   Again we switch to another


Yeah , we ju just to be clear , the numbers here are uh recognition accuracy.

w Yeah , this  Yeah , this number recognition acc

So it 's not the   Again we switch to another

Yes , and the baseline  the baseline have  i is eighty - two.


w Yeah , this  Yeah , this number recognition acc

So it 's not the   Again we switch to another

Yes , and the baseline  the baseline have  i is eighty - two.


So it 's not the   Again we switch to another

Yes , and the baseline  the baseline have  i is eighty - two.


Yes , and the baseline  the baseline have  i is eighty - two.

So it 's experiment only on the Italian mismatched for the moment for this.


So it 's experiment only on the Italian mismatched for the moment for this.


And first in the experiment - one I  I do  I  I use different MLP ,


And first in the experiment - one I  I do  I  I use different MLP ,

and is obviously that the multi - English MLP is the better. Um. for the ne  rest of experiment I use multi - English , only multi - English. 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.


And first in the experiment - one I  I do  I  I use different MLP ,

and is obviously that the multi - English MLP is the better. Um. for the ne  rest of experiment I use multi - English , only multi - English. 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.

Yeah , eh , actually , if w we look at the table , the huge table , um , we see that for TI - digits MSG perform as well as the PLP ,


and is obviously that the multi - English MLP is the better. Um. for the ne  rest of experiment I use multi - English , only multi - English. 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.

Yeah , eh , actually , if w we look at the table , the huge table , um , we see that for TI - digits MSG perform as well as the PLP ,


and is obviously that the multi - English MLP is the better. Um. for the ne  rest of experiment I use multi - English , only multi - English. 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.

Yeah , eh , actually , if w we look at the table , the huge table , um , we see that for TI - digits MSG perform as well as the PLP ,

but this is not the case for Italian what  where the error rate is c is almost uh twice the error rate of PLP.


Yeah , eh , actually , if w we look at the table , the huge table , um , we see that for TI - digits MSG perform as well as the PLP ,

but this is not the case for Italian what  where the error rate is c is almost uh twice the error rate of PLP.


but this is not the case for Italian what  where the error rate is c is almost uh twice the error rate of PLP.

So , um  uh , well , I don't think this is a bug but this  this is something in  probably in the MSG um process that uh 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 uh get improvement by combining PLP and MSG


So , um  uh , well , I don't think this is a bug but this  this is something in  probably in the MSG um process that uh 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 uh get improvement by combining PLP and MSG

because for the moment MSG do doesn't bring much information.


because for the moment MSG do doesn't bring much information.

And as Carmen said , if we combine the two , we have the result , basically , of PLP.


because for the moment MSG do doesn't bring much information.

And as Carmen said , if we combine the two , we have the result , basically , of PLP.

I Um , the uh , baseline system  when you said the baseline system was uh , uh eighty - two percent , that was trained on what and tested on what ? That was , uh Italian mismatched d uh , uh , digits , uh , is the testing ,


And as Carmen said , if we combine the two , we have the result , basically , of PLP.

I Um , the uh , baseline system  when you said the baseline system was uh , uh eighty - two percent , that was trained on what and tested on what ? That was , uh Italian mismatched d uh , uh , digits , uh , is the testing ,


I Um , the uh , baseline system  when you said the baseline system was uh , uh eighty - two percent , that was trained on what and tested on what ? That was , uh Italian mismatched d uh , uh , digits , uh , is the testing ,


So the " mismatch " just refers to the noise and  and , uh microphone and so forth ,


So the " mismatch " just refers to the noise and  and , uh microphone and so forth ,

right ? So , um did we have  So would that then correspond to the first line here of where the training is  is the uh Italian digits ?


right ? So , um did we have  So would that then correspond to the first line here of where the training is  is the uh Italian digits ?


yeah. So , um  So what that says is that in a matched condition ,  we end up with a fair amount worse putting in the uh PLP. Now w would  do we have a number , I suppose for the matched  I  I don't mean matched , but uh use of Italian  training in Italian digits for PLP only ?


yeah. So , um  So what that says is that in a matched condition ,  we end up with a fair amount worse putting in the uh PLP. Now w would  do we have a number , I suppose for the matched  I  I don't mean matched , but uh use of Italian  training in Italian digits for PLP only ?

Uh  yeah , so this is  basically this is in the table. Uh  so the number is fifty - two ,


Uh  yeah , so this is  basically this is in the table. Uh  so the number is fifty - two ,


No , fifty - two percent of eighty - two ?


Uh , so we have nine  nine  let 's say ninety percent.


Uh , so we have nine  nine  let 's say ninety percent.

Yeah. Um  which is uh  what we have also if use PLP and MSG together ,


Yeah. Um  which is uh  what we have also if use PLP and MSG together ,


Yeah. Um  which is uh  what we have also if use PLP and MSG together ,

OK , so even just PLP , uh , it is not , in the matched condition  Um 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.


OK , so even just PLP , uh , it is not , in the matched condition  Um 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.

Uh. P - PLP and Mel cepstra give the same  same results.


OK , so even just PLP , uh , it is not , in the matched condition  Um 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.

Uh. P - PLP and Mel cepstra give the same  same results.

Well , we have these results. I don't know. It 's not  Do you have this result with PLP alone ,  j fee feeding HTK ?


Uh. P - PLP and Mel cepstra give the same  same results.

Well , we have these results. I don't know. It 's not  Do you have this result with PLP alone ,  j fee feeding HTK ?


Well , we have these results. I don't know. It 's not  Do you have this result with PLP alone ,  j fee feeding HTK ?


yeah yeah yeah yeah , at the first  and the  Yeah.


um  Well , but that 's  yeah , that 's without the neural net ,


um  Well , but that 's  yeah , that 's without the neural net ,

and that 's the result basically that OGI has also with the MFCC with on - line normalization.


and that 's the result basically that OGI has also with the MFCC with on - line normalization.


and that 's the result basically that OGI has also with the MFCC with on - line normalization.

This is the  w well , but this is without on - line normalization.


This is the  w well , but this is without on - line normalization.


This is the  w well , but this is without on - line normalization.

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


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

Oh , I 'm sorry , I k I keep getting confused because this is accuracy.


Oh , I 'm sorry , I k I keep getting confused because this is accuracy.


Alright. So this is  I was thinking all this was worse. OK so this is all better


Alright. So this is  I was thinking all this was worse. OK so this is all better

because eighty - nine is bigger than eighty - two.


because eighty - nine is bigger than eighty - two.


because eighty - nine is bigger than eighty - two.

OK. I 'm  I 'm all better now. OK , go ahead.


OK. I 'm  I 'm all better now. OK , go ahead.

So what happ what happens is that when we apply on - line normalization we jump to almost ninety percent.


OK. I 'm  I 'm all better now. OK , go ahead.

So what happ what happens is that when we apply on - line normalization we jump to almost ninety percent.

Uh , when we apply a neural network , is the same. We j jump to ninety percent.


So what happ what happens is that when we apply on - line normalization we jump to almost ninety percent.

Uh , when we apply a neural network , is the same. We j jump to ninety percent.


Uh , when we apply a neural network , is the same. We j jump to ninety percent.


Uh , when we apply a neural network , is the same. We j jump to ninety percent.

And  And um  whatever the normalization , actually. If we use n neural network , even if the features are not correctly normalized , we jump to ninety percent. So


And  And um  whatever the normalization , actually. If we use n neural network , even if the features are not correctly normalized , we jump to ninety percent. So

So we go from eighty - si eighty - eight point six to  to ninety , or something.


And  And um  whatever the normalization , actually. If we use n neural network , even if the features are not correctly normalized , we jump to ninety percent. So

So we go from eighty - si eighty - eight point six to  to ninety , or something.

Well , ninety  No , I  I mean ninety It 's around eighty - nine , ninety , eighty - eight.


So we go from eighty - si eighty - eight point six to  to ninety , or something.

Well , ninety  No , I  I mean ninety It 's around eighty - nine , ninety , eighty - eight.

Well , there are minor  minor differences.


Well , ninety  No , I  I mean ninety It 's around eighty - nine , ninety , eighty - eight.

Well , there are minor  minor differences.


Well , there are minor  minor differences.

And then adding the MSG does nothing , basically.


And then adding the MSG does nothing , basically.


Alright. So , um  So actually , the answer for experiments with one is that adding MSG , if you  uh does not help in that case.


The other ones , we 'd have to look at it , but  And the multi - English , does uh  So if we think of this in error rates , we start off with , uh eighteen percent error rate , roughly.


The other ones , we 'd have to look at it , but  And the multi - English , does uh  So if we think of this in error rates , we start off with , uh eighteen percent error rate , roughly.

Um  and  we uh almost , uh cut that in half by um putting in the on - line normalization and the neural net.


Um  and  we uh almost , uh cut that in half by um putting in the on - line normalization and the neural net.

And the MSG doesn't however particularly affect things.


And the MSG doesn't however particularly affect things.

And we cut off , I guess about twenty - five percent of the error. Uh  no , not quite that , is it. Uh , two point six out of eighteen. About , um  sixteen percent or something of the error , um , if we use multi - English instead of the matching condition.


And we cut off , I guess about twenty - five percent of the error. Uh  no , not quite that , is it. Uh , two point six out of eighteen. About , um  sixteen percent or something of the error , um , if we use multi - English instead of the matching condition.

Not matching condition , but uh , the uh , Italian training.


Not matching condition , but uh , the uh , Italian training.


We select these  these  these tasks because it 's the more difficult.


We select these  these  these tasks because it 's the more difficult.

Yes , good. 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 , uh , data for the  uh for the new  for the other languages and so forth. Um , one qu thing is that , uh  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 ,


We select these  these  these tasks because it 's the more difficult.

Yes , good. 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 , uh , data for the  uh for the new  for the other languages and so forth. Um , one qu thing is that , uh  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 ,

but it is not all of the multi - English , right ? It is some piece of  part of it.


Yes , good. 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 , uh , data for the  uh for the new  for the other languages and so forth. Um , one qu thing is that , uh  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 ,

but it is not all of the multi - English , right ? It is some piece of  part of it.


but it is not all of the multi - English , right ? It is some piece of  part of it.


Oh , so you used almost all You used two thirds of it ,


Oh , so you used almost all You used two thirds of it ,

you think. So , it it 's still  it hurts you  seems to hurt you a fair amount to add in this French and Spanish.


you think. So , it it 's still  it hurts you  seems to hurt you a fair amount to add in this French and Spanish.


Well Stephane was saying that they weren't hand - labeled ,


It 's still  OK. Alright , go ahead. And then  then


It 's still  OK. Alright , go ahead. And then  then

Um. Mmm , with the experiment type - two , I  first I tried to to combine , nnn , some feature from the MLP and other feature  another feature.


It 's still  OK. Alright , go ahead. And then  then

Um. Mmm , with the experiment type - two , I  first I tried to to combine , nnn , some feature from the MLP and other feature  another feature.

And we s we can  first the feature are without delta and delta - delta , and we can see that in the situation , uh , the MSG - three , the same help nothing.


Um. Mmm , with the experiment type - two , I  first I tried to to combine , nnn , some feature from the MLP and other feature  another feature.

And we s we can  first the feature are without delta and delta - delta , and we can see that in the situation , uh , the MSG - three , the same help nothing.


And we s we can  first the feature are without delta and delta - delta , and we can see that in the situation , uh , the MSG - three , the same help nothing.

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.


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.

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. I don't know.


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. I don't know.


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. I don't know.

Do This number , this eighty - seven point one number , has to be compared with the


Do This number , this eighty - seven point one number , has to be compared with the

Yes , yeah , I mean it can't be compared with the other


Do This number , this eighty - seven point one number , has to be compared with the

Yes , yeah , I mean it can't be compared with the other

cuz this is , uh  with multi - English , uh , training.


Yes , yeah , I mean it can't be compared with the other

cuz this is , uh  with multi - English , uh , training.


cuz this is , uh  with multi - English , uh , training.

So you have to compare it with the one over that you 've got in a box , which is that , uh the eighty - four point six.


So you have to compare it with the one over that you 've got in a box , which is that , uh the eighty - four point six.


Yeah , but I mean in this case for the eighty - seven point one we used MLP outputs for the PLP net


Yeah , but I mean in this case for the eighty - seven point one we used MLP outputs for the PLP net

and straight features with delta - delta. And straight features with delta - delta gives you what 's on the first sheet.


and straight features with delta - delta. And straight features with delta - delta gives you what 's on the first sheet.


Uh , yeah , but th this is the second configuration.


Uh , yeah , but th this is the second configuration.

So we use feature out uh , 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.


Uh , yeah , but th this is the second configuration.

So we use feature out uh , 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.

Eh.  Oh , I see. Ah. So you 're saying w so asking the question , " What  what has adding the MLP done to improve over the ,


So we use feature out uh , 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.

Eh.  Oh , I see. Ah. So you 're saying w so asking the question , " What  what has adding the MLP done to improve over the ,

So , just  Yeah so , actually it  it  it decreased the  the accuracy.


Eh.  Oh , I see. Ah. So you 're saying w so asking the question , " What  what has adding the MLP done to improve over the ,

So , just  Yeah so , actually it  it  it decreased the  the accuracy.


So , just  Yeah so , actually it  it  it decreased the  the accuracy.


Because we have eighty - eight point six.


Because we have eighty - eight point six.

And even the MLP alone  What gives the MLP alone ? Multi - English PLP. Oh no , it gives eighty - three point six. So we have our eighty - three point six and now eighty - eighty point six ,


And even the MLP alone  What gives the MLP alone ? Multi - English PLP. Oh no , it gives eighty - three point six. So we have our eighty - three point six and now eighty - eighty point six ,


And even the MLP alone  What gives the MLP alone ? Multi - English PLP. Oh no , it gives eighty - three point six. So we have our eighty - three point six and now eighty - eighty point six ,

Mm - hmm. Eighty - s I thought it was eighty Oh , OK , eighty - three point six and eighty  eighty - eight point six.


Mm - hmm. Eighty - s I thought it was eighty Oh , OK , eighty - three point six and eighty  eighty - eight point six.


Yeah. 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.


Yeah. 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.

Well , that 's  that 's one thing , but see the other thing is that , um , I mean 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  I mean 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.


Well , that 's  that 's one thing , but see the other thing is that , um , I mean 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  I mean 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.

When we train on something that 's quite different , we have a potential to have some problems.


When we train on something that 's quite different , we have a potential to have some problems.

And , um , 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.


And , um , 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.

So the question is , if you took the same combination , and you tried it out on , uh  on say digits ,


So the question is , if you took the same combination , and you tried it out on , uh  on say digits ,


Yeah , OK. Uh , then does that , eh  you know maybe with similar noise conditions and so forth ,  does it  does it then look much better ?


Yeah , OK. Uh , then does that , eh  you know maybe with similar noise conditions and so forth ,  does it  does it then look much better ?

And so what is the range over these different kinds of uh  of tests ? So , an anyway. OK , go ahead.


And so what is the range over these different kinds of uh  of tests ? So , an anyway. OK , go ahead.

And , with this type of configuration which I do on experiment using the new neural net with name broad klatt s twenty - seven , uh , d I have found more or less the same result.


And , with this type of configuration which I do on experiment using the new neural net with name broad klatt s twenty - seven , uh , d I have found more or less the same result.


And  and you know again maybe if you use the , uh , delta  there , uh , you would bring it up to where it was , uh you know at least about the same for a difficult case.


Yeah. Well , so perhaps let 's  let 's jump at the last experiment.


Yeah. Well , so perhaps let 's  let 's jump at the last experiment.

It 's either less information from the neural network if we use only the silence output.


It 's either less information from the neural network if we use only the silence output.


It 's either less information from the neural network if we use only the silence output.

It 's again better. So it 's eighty - nine point  point one.


It 's again better. So it 's eighty - nine point  point one.


because in this situation we have one hundred and three feature.


because in this situation we have one hundred and three feature.

Yeah. And then w with the first configuration , I f I am found that work , uh , doesn't work


Yeah. And then w with the first configuration , I f I am found that work , uh , doesn't work

uh , well , work , but is better , the second configuration. 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.


Yeah. And then w with the first configuration , I f I am found that work , uh , doesn't work

uh , well , work , but is better , the second configuration. 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.

Um , by the way , there is a another , uh , suggestion that would apply , uh , to the second configuration , um , which , uh , was made , uh , by , uh , Hari. And that was that , um , if you have  uh feed two streams into HTK , um , and you , uh , change the , uh variances  if you scale the variances associated with , uh these streams um , you can effectively scale  the streams. Right ? So , um , you know , without changing the scripts for HTK , which is the rule here , uh , you can still change the variances


uh , well , work , but is better , the second configuration. 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.

Um , by the way , there is a another , uh , suggestion that would apply , uh , to the second configuration , um , which , uh , was made , uh , by , uh , Hari. And that was that , um , if you have  uh feed two streams into HTK , um , and you , uh , change the , uh variances  if you scale the variances associated with , uh these streams um , you can effectively scale  the streams. Right ? So , um , you know , without changing the scripts for HTK , which is the rule here , uh , you can still change the variances


uh , well , work , but is better , the second configuration. 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.

Um , by the way , there is a another , uh , suggestion that would apply , uh , to the second configuration , um , which , uh , was made , uh , by , uh , Hari. And that was that , um , if you have  uh feed two streams into HTK , um , and you , uh , change the , uh variances  if you scale the variances associated with , uh these streams um , you can effectively scale  the streams. Right ? So , um , you know , without changing the scripts for HTK , which is the rule here , uh , you can still change the variances

which would effectively change the scale of these  these , uh , two streams that come in.


Um , by the way , there is a another , uh , suggestion that would apply , uh , to the second configuration , um , which , uh , was made , uh , by , uh , Hari. And that was that , um , if you have  uh feed two streams into HTK , um , and you , uh , change the , uh variances  if you scale the variances associated with , uh these streams um , you can effectively scale  the streams. Right ? So , um , you know , without changing the scripts for HTK , which is the rule here , uh , you can still change the variances

which would effectively change the scale of these  these , uh , two streams that come in.


which would effectively change the scale of these  these , uh , two streams that come in.

And , um , so , um , if you do that , for instance it may be the case that , um , the MLP should not be considered as strongly , for instance.


And , um , so , um , if you do that , for instance it may be the case that , um , the MLP should not be considered as strongly , for instance.

And , um , so this is just setting them to be , excuse me , of equal  equal weight. Maybe it shouldn't be equal weight.


And , um , so this is just setting them to be , excuse me , of equal  equal weight. Maybe it shouldn't be equal weight.

Right ? You know , I I 'm sorry to say that gives more experiments if we wanted to look at that , but  but , uh , um , you know on the other hand it 's just experiments at the level of the HTK recognition.


Right ? You know , I I 'm sorry to say that gives more experiments if we wanted to look at that , but  but , uh , um , you know on the other hand it 's just experiments at the level of the HTK recognition.


Well , I guess you have to do the HTK training also.


Well , I guess you have to do the HTK training also.

Uh , do you ? Let me think. Maybe you don't. Uh. Yeah , you have to change the  No , you can just do it in  as  once you 've done the training


Uh , do you ? Let me think. Maybe you don't. Uh. Yeah , you have to change the  No , you can just do it in  as  once you 've done the training

Yeah , the training is just coming up with the variances so I guess you could  you could just scale them all.


Yeah , the training is just coming up with the variances so I guess you could  you could just scale them all.


Yeah , the training is just coming up with the variances so I guess you could  you could just scale them all.

Yeah. But  Is it  i th I mean the HTK models are diagonal covariances , so I d Is it


Yeah. But  Is it  i th I mean the HTK models are diagonal covariances , so I d Is it

That 's uh , exactly the point , I think , that if you change  um , change what they are


Yeah. But  Is it  i th I mean the HTK models are diagonal covariances , so I d Is it

That 's uh , exactly the point , I think , that if you change  um , change what they are

It 's diagonal covariance matrices , but you say what those variances are.


That 's uh , exactly the point , I think , that if you change  um , change what they are

It 's diagonal covariance matrices , but you say what those variances are.


It 's diagonal covariance matrices , but you say what those variances are.

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 uh ,  uh , i it im uh 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.


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 uh ,  uh , i it im uh 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.


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 uh ,  uh , i it im uh 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.

So , i it 's precisely given that model you can very simply affect , uh , the s the strength that you apply the features. That was  that was , uh , Hari 's suggestion.


So , i it 's precisely given that model you can very simply affect , uh , the s the strength that you apply the features. That was  that was , uh , Hari 's suggestion.


Yeah. So. 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 ,


So I guess the other thing is to take  you know  if one were to take , uh , you know , a couple of the most successful of these ,


So I guess the other thing is to take  you know  if one were to take , uh , you know , a couple of the most successful of these ,

and uh  Yeah , try all these different tests.


and uh  Yeah , try all these different tests.


So , the next point , yeah , we 've had some discussion with Steve and Shawn , um , about their um , uh , articulatory stuff , um. So we 'll perhaps start something next week.


So , the next point , yeah , we 've had some discussion with Steve and Shawn , um , about their um , uh , articulatory stuff , um. So we 'll perhaps start something next week.

Um , discussion with Hynek , Sunil and Pratibha for trying to plug in their our  our networks with their  within their block diagram , uh , where to plug in the  the network , uh , after the  the feature , before as um a as a plugin or as a anoth another path , discussion about multi - band and TRAPS , um , actually Hynek would like to see , perhaps if you remember the block diagram there is , uh , temporal LDA followed b by a spectral LDA for each uh critical band. And he would like to replace these by a network which would , uh , make the system look like a TRAP. Well , basically , it would be a TRAP system. Basically , this is a TRAP system  kind of TRAP system , I mean , but where the neural network are replaced by LDA. Hmm.  Um , yeah , and about multi - band , uh , I started multi - band MLP trainings , um 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. And , mmm , I 'm starting to train also , networks with larger contexts. 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 Um Yeah , we still have to work on Finnish , um , basically , to make a decision on which MLP can be the best across the different languages. For the moment it 's the TIMIT network , and perhaps the network trained on everything. So. Now we can test these two networks on  with  with delta and large networks. Well , test them also on Finnish


Um , discussion with Hynek , Sunil and Pratibha for trying to plug in their our  our networks with their  within their block diagram , uh , where to plug in the  the network , uh , after the  the feature , before as um a as a plugin or as a anoth another path , discussion about multi - band and TRAPS , um , actually Hynek would like to see , perhaps if you remember the block diagram there is , uh , temporal LDA followed b by a spectral LDA for each uh critical band. And he would like to replace these by a network which would , uh , make the system look like a TRAP. Well , basically , it would be a TRAP system. Basically , this is a TRAP system  kind of TRAP system , I mean , but where the neural network are replaced by LDA. Hmm.  Um , yeah , and about multi - band , uh , I started multi - band MLP trainings , um 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. And , mmm , I 'm starting to train also , networks with larger contexts. 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 Um Yeah , we still have to work on Finnish , um , basically , to make a decision on which MLP can be the best across the different languages. For the moment it 's the TIMIT network , and perhaps the network trained on everything. So. Now we can test these two networks on  with  with delta and large networks. Well , test them also on Finnish

and see which one is the  the  the best. Uh , well , the next part of the document is , well , basically , a kind of summary of what  everything that has been done. So. We have seventy - nine M L Ps trained on one , two , three , four , uh , three , four , five , six , seven ten  on ten different databases.


and see which one is the  the  the best. Uh , well , the next part of the document is , well , basically , a kind of summary of what  everything that has been done. So. We have seventy - nine M L Ps trained on one , two , three , four , uh , three , four , five , six , seven ten  on ten different databases.

Uh , 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. Uh , yeah !  As we mentioned , TIMIT is the only that 's hand - labeled , and perhaps this is what makes the difference. Um. Yeah , the other are just Viterbi - aligned. So these seventy - nine MLP differ on different things. First , um with respect to the on - line normalization , there are  that use bad on - line normalization , and other good on - line normalization. Um. With respect to the features , with respect to the use of delta or no , uh with respect to the hidden layer size and to the targets. Uh , but of course we don't have all the combination of these different parameters Um. s What 's this ? We only have two hundred eighty six different tests And no not two thousand.


and see which one is the  the  the best. Uh , well , the next part of the document is , well , basically , a kind of summary of what  everything that has been done. So. We have seventy - nine M L Ps trained on one , two , three , four , uh , three , four , five , six , seven ten  on ten different databases.

Uh , 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. Uh , yeah !  As we mentioned , TIMIT is the only that 's hand - labeled , and perhaps this is what makes the difference. Um. Yeah , the other are just Viterbi - aligned. So these seventy - nine MLP differ on different things. First , um with respect to the on - line normalization , there are  that use bad on - line normalization , and other good on - line normalization. Um. With respect to the features , with respect to the use of delta or no , uh with respect to the hidden layer size and to the targets. Uh , but of course we don't have all the combination of these different parameters Um. s What 's this ? We only have two hundred eighty six different tests And no not two thousand.

Ugh ! I was impressed boy , two thousand.


Uh , 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. Uh , yeah !  As we mentioned , TIMIT is the only that 's hand - labeled , and perhaps this is what makes the difference. Um. Yeah , the other are just Viterbi - aligned. So these seventy - nine MLP differ on different things. First , um with respect to the on - line normalization , there are  that use bad on - line normalization , and other good on - line normalization. Um. With respect to the features , with respect to the use of delta or no , uh with respect to the hidden layer size and to the targets. Uh , but of course we don't have all the combination of these different parameters Um. s What 's this ? We only have two hundred eighty six different tests And no not two thousand.

Ugh ! I was impressed boy , two thousand.


Ugh ! I was impressed boy , two thousand.


I say this morning that @ @ thought it was the


I say this morning that @ @ thought it was the

Alright , now I 'm just slightly impressed , OK.


I say this morning that @ @ thought it was the

Alright , now I 'm just slightly impressed , OK.

Um. Yeah , basically the observation is what we discussed already. The MSG problem , um , the fact that the MLP trained on target task decreased the error rate. but when the M - MLP is trained on the um  is not trained on the target task , it increased the error rate compared to using straight features. Except if the features are bad  uh , 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.


I say this morning that @ @ thought it was the

Alright , now I 'm just slightly impressed , OK.

Um. Yeah , basically the observation is what we discussed already. The MSG problem , um , the fact that the MLP trained on target task decreased the error rate. but when the M - MLP is trained on the um  is not trained on the target task , it increased the error rate compared to using straight features. Except if the features are bad  uh , 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.

Yeah. So it sounds like  yeah , the net corrects some of the problems with some poor normalization.


Alright , now I 'm just slightly impressed , OK.

Um. Yeah , basically the observation is what we discussed already. The MSG problem , um , the fact that the MLP trained on target task decreased the error rate. but when the M - MLP is trained on the um  is not trained on the target task , it increased the error rate compared to using straight features. Except if the features are bad  uh , 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.

Yeah. So it sounds like  yeah , the net corrects some of the problems with some poor normalization.


Um. Yeah , basically the observation is what we discussed already. The MSG problem , um , the fact that the MLP trained on target task decreased the error rate. but when the M - MLP is trained on the um  is not trained on the target task , it increased the error rate compared to using straight features. Except if the features are bad  uh , 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.

Yeah. So it sounds like  yeah , the net corrects some of the problems with some poor normalization.

But if you can do good normalization it 's  it 's uh  OK.


Yeah. So it sounds like  yeah , the net corrects some of the problems with some poor normalization.

But if you can do good normalization it 's  it 's uh  OK.


But if you can do good normalization it 's  it 's uh  OK.


But if you can do good normalization it 's  it 's uh  OK.

Uh , so the fourth point is , yeah , the TIMIT plus noise seems to be the training set that gives better  the best network.


Uh , so the fourth point is , yeah , the TIMIT plus noise seems to be the training set that gives better  the best network.

So So - Let me  bef before you go on to the possible issues.


Uh , so the fourth point is , yeah , the TIMIT plus noise seems to be the training set that gives better  the best network.

So So - Let me  bef before you go on to the possible issues.

So , on the MSG uh problem um , I think that in  in the  um , in the short  time  solution um , that is , um , trying to figure out what we can proceed forward with to make the greatest progress ,


So So - Let me  bef before you go on to the possible issues.

So , on the MSG uh problem um , I think that in  in the  um , in the short  time  solution um , that is , um , trying to figure out what we can proceed forward with to make the greatest progress ,


So , on the MSG uh problem um , I think that in  in the  um , in the short  time  solution um , that is , um , trying to figure out what we can proceed forward with to make the greatest progress ,

uh , much as I said with JRASTA , even though I really like JRASTA and I really like MSG ,


uh , much as I said with JRASTA , even though I really like JRASTA and I really like MSG ,

I think it 's kind of in category that it 's , it  it may be complicated.


I think it 's kind of in category that it 's , it  it may be complicated.

And uh it might be  if someone 's interested in it , uh , certainly encourage anybody to look into it in the longer term , once we get out of this particular rush  uh for results.


And uh it might be  if someone 's interested in it , uh , certainly encourage anybody to look into it in the longer term , once we get out of this particular rush  uh for results.

But in the short term , unless you have some  some s strong idea of what 's wrong ,


And uh it might be  if someone 's interested in it , uh , certainly encourage anybody to look into it in the longer term , once we get out of this particular rush  uh for results.

But in the short term , unless you have some  some s strong idea of what 's wrong ,

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


But in the short term , unless you have some  some s strong idea of what 's wrong ,

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


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

There 's supposed to  well MSG is supposed to have a an on - line normalization though , right ?


There 's supposed to  well MSG is supposed to have a an on - line normalization though , right ?

It 's  There is , yeah , an AGC - kind of AGC. Yeah.  Yeah. Yeah.


There 's supposed to  well MSG is supposed to have a an on - line normalization though , right ?

It 's  There is , yeah , an AGC - kind of AGC. Yeah.  Yeah. Yeah.

Yeah , but also there 's an on - line norm besides the AGC , there 's an on - line normalization that 's supposed to be uh , yeah ,


It 's  There is , yeah , an AGC - kind of AGC. Yeah.  Yeah. Yeah.

Yeah , but also there 's an on - line norm besides the AGC , there 's an on - line normalization that 's supposed to be uh , yeah ,

taking out means and variances and so forth. So.


Yeah , but also there 's an on - line norm besides the AGC , there 's an on - line normalization that 's supposed to be uh , yeah ,

taking out means and variances and so forth. So.


taking out means and variances and so forth. So.

In fac in fact the on - line normalization that we 're using came from the MSG design ,


In fac in fact the on - line normalization that we 're using came from the MSG design ,


In fac in fact the on - line normalization that we 're using came from the MSG design ,

Yeah , but  Yeah. But this was the bad on - line normalization. Actually. Uh. Are your results are still with the bad  the bad


Yeah , but  Yeah. But this was the bad on - line normalization. Actually. Uh. Are your results are still with the bad  the bad


Ah yeah , you have  you have OLN - two ,


Ah yeah , you have  you have OLN - two ,

Oh ! Yeah , yeah , yeah ! With " two " , with " on - line - two ".


Oh ! Yeah , yeah , yeah ! With " two " , with " on - line - two ".


Well , actually , it 's good with the ch with the good.


Well , actually , it 's good with the ch with the good.

OK. Yeah. So  Yeah , I  I agree. It 's probably something simple uh , i if  if uh someone , you know , uh , wants to play with it for a little bit. I mean , you 're gonna do what you 're gonna do


Well , actually , it 's good with the ch with the good.

OK. Yeah. So  Yeah , I  I agree. It 's probably something simple uh , i if  if uh someone , you know , uh , wants to play with it for a little bit. I mean , you 're gonna do what you 're gonna do

but  but my  my guess would be that it 's something that is a simple thing that could take a while to find.


OK. Yeah. So  Yeah , I  I agree. It 's probably something simple uh , i if  if uh someone , you know , uh , wants to play with it for a little bit. I mean , you 're gonna do what you 're gonna do

but  but my  my guess would be that it 's something that is a simple thing that could take a while to find.


but  but my  my guess would be that it 's something that is a simple thing that could take a while to find.


Uh.  And the other  the results uh , observations two and three , Um , is


Uh.  And the other  the results uh , observations two and three , Um , is

uh  Yeah , that 's pretty much what we 've seen. 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.


uh  Yeah , that 's pretty much what we 've seen. 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.

If it uh  if it 's not on the target task , then , depending on how different it is , uh you can get uh , a reduction in performance.


If it uh  if it 's not on the target task , then , depending on how different it is , uh you can get uh , a reduction in performance.

And the question is now how to  how to get one and not the other ? Or how to  how to ameliorate the  the problems.


And the question is now how to  how to get one and not the other ? Or how to  how to ameliorate the  the problems.

Um , because it  it certainly does  is nice to have in there , when it   when there is something like the training data.


And the question is now how to  how to get one and not the other ? Or how to  how to ameliorate the  the problems.

Um , because it  it certainly does  is nice to have in there , when it   when there is something like the training data.

Mm - hmm. Um. Yeah. So ,  the  the reason  Yeah , the reason is that the  perhaps the target  the  the task dependency  the language dependency ,  and the noise dependency


Um , because it  it certainly does  is nice to have in there , when it   when there is something like the training data.

Mm - hmm. Um. Yeah. So ,  the  the reason  Yeah , the reason is that the  perhaps the target  the  the task dependency  the language dependency ,  and the noise dependency

So that 's what you say th there. I see.


Um , because it  it certainly does  is nice to have in there , when it   when there is something like the training data.

Mm - hmm. Um. Yeah. So ,  the  the reason  Yeah , the reason is that the  perhaps the target  the  the task dependency  the language dependency ,  and the noise dependency

So that 's what you say th there. I see.

Well , the e e But this is still not clear because , um , 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.


Mm - hmm. Um. Yeah. So ,  the  the reason  Yeah , the reason is that the  perhaps the target  the  the task dependency  the language dependency ,  and the noise dependency

So that 's what you say th there. I see.

Well , the e e But this is still not clear because , um , 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.

Hey ! Um , just  you can just sit here. Uh , I d I don't think we want to mess with the microphones but it 's uh  Just uh , have a seat. Um. s Summary of the first uh , uh forty - five minutes is that some stuff work and  works , and some stuff doesn't OK ,


So that 's what you say th there. I see.

Well , the e e But this is still not clear because , um , 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.

Hey ! Um , just  you can just sit here. Uh , I d I don't think we want to mess with the microphones but it 's uh  Just uh , have a seat. Um. s Summary of the first uh , uh forty - five minutes is that some stuff work and  works , and some stuff doesn't OK ,

We still have uh  this  One of these perhaps ?


Well , the e e But this is still not clear because , um , 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.

Hey ! Um , just  you can just sit here. Uh , I d I don't think we want to mess with the microphones but it 's uh  Just uh , have a seat. Um. s Summary of the first uh , uh forty - five minutes is that some stuff work and  works , and some stuff doesn't OK ,

We still have uh  this  One of these perhaps ?


Hey ! Um , just  you can just sit here. Uh , I d I don't think we want to mess with the microphones but it 's uh  Just uh , have a seat. Um. s Summary of the first uh , uh forty - five minutes is that some stuff work and  works , and some stuff doesn't OK ,

We still have uh  this  One of these perhaps ?


We still have uh  this  One of these perhaps ?

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.


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.


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.

OK. So you 're saying that um , um , although from what we see , yes there 's what you would expect in terms of a language dependency and a noise dependency. That is , uh , 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 uh , it is  Although that general thing is observable so far , there 's something you 're not completely convinced about. And  and what is that ? I mean , you say " not clear yet ". What  what do you mean ?


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.

OK. So you 're saying that um , um , although from what we see , yes there 's what you would expect in terms of a language dependency and a noise dependency. That is , uh , 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 uh , it is  Although that general thing is observable so far , there 's something you 're not completely convinced about. And  and what is that ? I mean , you say " not clear yet ". What  what do you mean ?

Uh , mmm , uh ,  I mean , that the  the fact that s Well , for  for TI - digits the TIMIT net is the best ,  which is the English net.


OK. So you 're saying that um , um , although from what we see , yes there 's what you would expect in terms of a language dependency and a noise dependency. That is , uh , 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 uh , it is  Although that general thing is observable so far , there 's something you 're not completely convinced about. And  and what is that ? I mean , you say " not clear yet ". What  what do you mean ?

Uh , mmm , uh ,  I mean , that the  the fact that s Well , for  for TI - digits the TIMIT net is the best ,  which is the English net.


OK. So you 're saying that um , um , although from what we see , yes there 's what you would expect in terms of a language dependency and a noise dependency. That is , uh , 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 uh , it is  Although that general thing is observable so far , there 's something you 're not completely convinced about. And  and what is that ? I mean , you say " not clear yet ". What  what do you mean ?

Uh , mmm , uh ,  I mean , that the  the fact that s Well , for  for TI - digits the TIMIT net is the best ,  which is the English net.

But the other are slightly worse. 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.


Uh , mmm , uh ,  I mean , that the  the fact that s Well , for  for TI - digits the TIMIT net is the best ,  which is the English net.

But the other are slightly worse. 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.


But the other are slightly worse. 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.


But the other are slightly worse. 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.

Do you think the alignments are bad ? I mean , have you looked at the alignments at all ? What the Viterbi alignment 's doing ?


Do you think the alignments are bad ? I mean , have you looked at the alignments at all ? What the Viterbi alignment 's doing ?

Mmm. I don't  I don't know. Did - did you look at the Spanish alignments Carmen ?


Do you think the alignments are bad ? I mean , have you looked at the alignments at all ? What the Viterbi alignment 's doing ?

Mmm. I don't  I don't know. Did - did you look at the Spanish alignments Carmen ?

Might be interesting to look at it. Because , I mean , that is just looking but um , um  It 's not clear to me you necessarily would do so badly from a Viterbi alignment. It depends how good the recognizer is


Mmm. I don't  I don't know. Did - did you look at the Spanish alignments Carmen ?

Might be interesting to look at it. Because , I mean , that is just looking but um , um  It 's not clear to me you necessarily would do so badly from a Viterbi alignment. It depends how good the recognizer is


Might be interesting to look at it. Because , I mean , that is just looking but um , um  It 's not clear to me you necessarily would do so badly from a Viterbi alignment. It depends how good the recognizer is

that 's  that  the  the engine is that 's doing the alignment.


Might be interesting to look at it. Because , I mean , that is just looking but um , um  It 's not clear to me you necessarily would do so badly from a Viterbi alignment. It depends how good the recognizer is

that 's  that  the  the engine is that 's doing the alignment.

Yeah. But  Yeah. 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


that 's  that  the  the engine is that 's doing the alignment.

Yeah. But  Yeah. 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


Yeah. But  Yeah. 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


I mean  for  We  It 's single pronunciation , uh


I mean  for  We  It 's single pronunciation , uh

French  French s uh , phoneme strings were corrected manually


French  French s uh , phoneme strings were corrected manually

so we asked people to listen to the um  the sentence and we gave the phoneme string and they kind of correct them. But still ,  there  there might be errors just in the  in  in the ph string of phonemes. Mmm. Um. Yeah , so this is not really the Viterbi alignment ,  in fact , yeah. Um , the third  The third uh issue is the noise dependency perhaps but , well , this is not clear yet because all our nets are trained on the same noises and


French  French s uh , phoneme strings were corrected manually

so we asked people to listen to the um  the sentence and we gave the phoneme string and they kind of correct them. But still ,  there  there might be errors just in the  in  in the ph string of phonemes. Mmm. Um. Yeah , so this is not really the Viterbi alignment ,  in fact , yeah. Um , the third  The third uh issue is the noise dependency perhaps but , well , this is not clear yet because all our nets are trained on the same noises and

I thought some of the nets were trained with SPINE and so forth. So it  And that has other noise.


so we asked people to listen to the um  the sentence and we gave the phoneme string and they kind of correct them. But still ,  there  there might be errors just in the  in  in the ph string of phonemes. Mmm. Um. Yeah , so this is not really the Viterbi alignment ,  in fact , yeah. Um , the third  The third uh issue is the noise dependency perhaps but , well , this is not clear yet because all our nets are trained on the same noises and

I thought some of the nets were trained with SPINE and so forth. So it  And that has other noise.

Yeah. So  Yeah. But  Yeah. Results are only coming for  for this net. Mmm.


so we asked people to listen to the um  the sentence and we gave the phoneme string and they kind of correct them. But still ,  there  there might be errors just in the  in  in the ph string of phonemes. Mmm. Um. Yeah , so this is not really the Viterbi alignment ,  in fact , yeah. Um , the third  The third uh issue is the noise dependency perhaps but , well , this is not clear yet because all our nets are trained on the same noises and

I thought some of the nets were trained with SPINE and so forth. So it  And that has other noise.

Yeah. So  Yeah. But  Yeah. Results are only coming for  for this net. Mmm.

OK , yeah , just don't  just need more  more results there with that @ @.


I thought some of the nets were trained with SPINE and so forth. So it  And that has other noise.

Yeah. So  Yeah. But  Yeah. Results are only coming for  for this net. Mmm.

OK , yeah , just don't  just need more  more results there with that @ @.

Yeah. Um. So. Uh , from these results we have some questions with answers. What should be the network input ? Um , PLP work as well as MFCC , I mean. Um. But it seems impor important to use the delta. Uh , 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. So , nnn , still no answer actually.


Yeah. So  Yeah. But  Yeah. Results are only coming for  for this net. Mmm.

OK , yeah , just don't  just need more  more results there with that @ @.

Yeah. Um. So. Uh , from these results we have some questions with answers. What should be the network input ? Um , PLP work as well as MFCC , I mean. Um. But it seems impor important to use the delta. Uh , 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. So , nnn , still no answer actually.


OK , yeah , just don't  just need more  more results there with that @ @.

Yeah. Um. So. Uh , from these results we have some questions with answers. What should be the network input ? Um , PLP work as well as MFCC , I mean. Um. But it seems impor important to use the delta. Uh , 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. So , nnn , still no answer actually.

Uh , the training set , well , some kind of answer. We can , we can tell which training set gives the best result , but  we don't know exactly why. Uh , so.


Yeah. Um. So. Uh , from these results we have some questions with answers. What should be the network input ? Um , PLP work as well as MFCC , I mean. Um. But it seems impor important to use the delta. Uh , 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. So , nnn , still no answer actually.

Uh , the training set , well , some kind of answer. We can , we can tell which training set gives the best result , but  we don't know exactly why. Uh , so.

Uh. Right , I mean the multi - English so far is  is the best.


Uh , the training set , well , some kind of answer. We can , we can tell which training set gives the best result , but  we don't know exactly why. Uh , so.

Uh. Right , I mean the multi - English so far is  is the best.


Uh , the training set , well , some kind of answer. We can , we can tell which training set gives the best result , but  we don't know exactly why. Uh , so.

Uh. Right , I mean the multi - English so far is  is the best.

" Multi - multi - English " just means " TIMIT " ,


Uh. Right , I mean the multi - English so far is  is the best.

" Multi - multi - English " just means " TIMIT " ,


" Multi - multi - English " just means " TIMIT " ,


So uh That 's  Yeah. So. And  and when you add other things in to  to broaden it , it gets worse  uh typically.


I like that. The training set is both questions , with answers and without answers.


I like that. The training set is both questions , with answers and without answers.

It 's sort of , yes  it 's mul it 's multi - uh - purpose.


It 's sort of , yes  it 's mul it 's multi - uh - purpose.


It 's sort of , yes  it 's mul it 's multi - uh - purpose.

Uh , training s Right. 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.


Uh , training s Right. 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.


Uh , training s Right. 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.

And labeling s labeling seems important uh , because of TIMIT results.


And labeling s labeling seems important uh , because of TIMIT results.

Uh. For moment you use  we use phonetic targets but we could also use articulatory targets , soft targets , and perhaps even , um use networks that doesn't do classification but just regression so uh , train to have neural networks that um , um , uh ,


Uh. For moment you use  we use phonetic targets but we could also use articulatory targets , soft targets , and perhaps even , um use networks that doesn't do classification but just regression so uh , train to have neural networks that um , um , uh ,

does a regression and well , basically com com compute features and noit not , nnn , features without noise. I mean uh , transform the fea noisy features  in other features that are not noisy. But continuous features. Not uh uh , hard targets.


does a regression and well , basically com com compute features and noit not , nnn , features without noise. I mean uh , transform the fea noisy features  in other features that are not noisy. But continuous features. Not uh uh , hard targets.


does a regression and well , basically com com compute features and noit not , nnn , features without noise. I mean uh , transform the fea noisy features  in other features that are not noisy. But continuous features. Not uh uh , hard targets.

Yeah , that  seems like a good thing to do , probably ,  not uh again a short - term sort of thing.


Yeah , that  seems like a good thing to do , probably ,  not uh again a short - term sort of thing.


Yeah , that  seems like a good thing to do , probably ,  not uh again a short - term sort of thing.

I mean one of the things about that is that um 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.


I mean one of the things about that is that um 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.


I mean one of the things about that is that um 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.

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 um we could combine this with other approaches , like , well , the Kleinschmidt approach. So the d the idea of putting all the noise that we can find inside a database. I think Kleinschmidt was using more than fifty different noises to train his network ,


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 um we could combine this with other approaches , like , well , the Kleinschmidt approach. So the d the idea of putting all the noise that we can find inside a database. I think Kleinschmidt was using more than fifty different noises to train his network ,


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 um we could combine this with other approaches , like , well , the Kleinschmidt approach. So the d the idea of putting all the noise that we can find inside a database. I think Kleinschmidt was using more than fifty different noises to train his network ,

and  So this is one  approach and the other is multi - band   uh , that I think is more robust to the noisy changes.


and  So this is one  approach and the other is multi - band   uh , that I think is more robust to the noisy changes.


and  So this is one  approach and the other is multi - band   uh , that I think is more robust to the noisy changes.

So perhaps , I think something like multi - band trained on a lot of noises with uh , features - based targets could  could  could help.


and  So this is one  approach and the other is multi - band   uh , that I think is more robust to the noisy changes.

So perhaps , I think something like multi - band trained on a lot of noises with uh , features - based targets could  could  could help.

Yeah , if you  i i It 's interesting thought maybe if you just trained up  I mean w yeah , one  one fantasy would be you have something like articulatory targets and you have  um some reasonable database , um but then  which is um  copied over many times with a range of different noises ,


So perhaps , I think something like multi - band trained on a lot of noises with uh , features - based targets could  could  could help.

Yeah , if you  i i It 's interesting thought maybe if you just trained up  I mean w yeah , one  one fantasy would be you have something like articulatory targets and you have  um some reasonable database , um but then  which is um  copied over many times with a range of different noises ,


So perhaps , I think something like multi - band trained on a lot of noises with uh , features - based targets could  could  could help.

Yeah , if you  i i It 's interesting thought maybe if you just trained up  I mean w yeah , one  one fantasy would be you have something like articulatory targets and you have  um some reasonable database , um but then  which is um  copied over many times with a range of different noises ,

And uh   If  Cuz what you 're trying to  do is come up with a  a core , reasonable feature set which is then gonna be used uh , by the  the uh HMM  system.


Yeah , if you  i i It 's interesting thought maybe if you just trained up  I mean w yeah , one  one fantasy would be you have something like articulatory targets and you have  um some reasonable database , um but then  which is um  copied over many times with a range of different noises ,

And uh   If  Cuz what you 're trying to  do is come up with a  a core , reasonable feature set which is then gonna be used uh , by the  the uh HMM  system.


And uh   If  Cuz what you 're trying to  do is come up with a  a core , reasonable feature set which is then gonna be used uh , by the  the uh HMM  system.


And uh   If  Cuz what you 're trying to  do is come up with a  a core , reasonable feature set which is then gonna be used uh , by the  the uh HMM  system.

So , um , yeah. The future work is ,  well , try to connect to the  to make  to plug in the system to the OGI system. Um , there are still open questions there , where to put the MLP basically.


So , um , yeah. The future work is ,  well , try to connect to the  to make  to plug in the system to the OGI system. Um , there are still open questions there , where to put the MLP basically.


So , um , yeah. The future work is ,  well , try to connect to the  to make  to plug in the system to the OGI system. Um , there are still open questions there , where to put the MLP basically.

And I guess , you know , the  the  the real open question , I mean , e u there 's lots of open questions , but one of the core quote  " open questions " for that is um , um , if we take the uh  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. Um , how well do they do over a range of these different tests , not just the Italian ?


And I guess , you know , the  the  the real open question , I mean , e u there 's lots of open questions , but one of the core quote  " open questions " for that is um , um , if we take the uh  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. Um , how well do they do over a range of these different tests , not just the Italian ?


y  Right ? And then um  then see ,  again , how  We know that there 's a mis there 's a uh  a  a loss in performance when the neural net is trained on conditions that are different than  than , uh we 're gonna test on , but well , if you look over a range of these different tests um , how well do these different ways of combining the straight features with the MLP features , uh stand up over that range ?


y  Right ? And then um  then see ,  again , how  We know that there 's a mis there 's a uh  a  a loss in performance when the neural net is trained on conditions that are different than  than , uh we 're gonna test on , but well , if you look over a range of these different tests um , how well do these different ways of combining the straight features with the MLP features , uh stand up over that range ?

That 's  that  that seems like the  the  the real question. And if you know that  So if you just take PLP with uh , the double - deltas. Assume that 's the p the feature. look at these different ways of combining it. And uh , take  let 's say , just take uh multi - English cause that works pretty well for the training.


That 's  that  that seems like the  the  the real question. And if you know that  So if you just take PLP with uh , the double - deltas. Assume that 's the p the feature. look at these different ways of combining it. And uh , take  let 's say , just take uh multi - English cause that works pretty well for the training.

And just look  take that case and then look over all the different things. How does that  How does that compare between the


That 's  that  that seems like the  the  the real question. And if you know that  So if you just take PLP with uh , the double - deltas. Assume that 's the p the feature. look at these different ways of combining it. And uh , take  let 's say , just take uh multi - English cause that works pretty well for the training.

And just look  take that case and then look over all the different things. How does that  How does that compare between the

So all the  all the test sets you mean , yeah.


And just look  take that case and then look over all the different things. How does that  How does that compare between the

So all the  all the test sets you mean , yeah.


So all the  all the test sets you mean , yeah.


and for  and for the couple different ways that you have of  of  of combining them.


and for  and for the couple different ways that you have of  of  of combining them.

Um.  How well do they stand up , over the


and for  and for the couple different ways that you have of  of  of combining them.

Um.  How well do they stand up , over the

Mmm. And perhaps doing this for  cha changing the variance of the streams and so on  getting different scaling


Um.  How well do they stand up , over the

Mmm. And perhaps doing this for  cha changing the variance of the streams and so on  getting different scaling


Um.  How well do they stand up , over the

Mmm. And perhaps doing this for  cha changing the variance of the streams and so on  getting different scaling

That 's another possibility if you have time , yeah. Yeah.


Mmm. And perhaps doing this for  cha changing the variance of the streams and so on  getting different scaling

That 's another possibility if you have time , yeah. Yeah.

Um. 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.


That 's another possibility if you have time , yeah. Yeah.

Um. 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.

Cuz  Yeah. 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 ,


Um. 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.

Cuz  Yeah. 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 ,


Cuz  Yeah. 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 ,

but on the other hand they did it before.


but on the other hand they did it before.


but on the other hand they did it before.

yeah. And because also perhaps we know that the  when we have very good features the MLP doesn't help. So. I don't know.


yeah. And because also perhaps we know that the  when we have very good features the MLP doesn't help. So. I don't know.

Um , the other thing , though , is that um  So. Uh , we  we wanna get their path running here , right ? If so , we can add this other stuff.


Um , the other thing , though , is that um  So. Uh , we  we wanna get their path running here , right ? If so , we can add this other stuff.


Yeah , the way we want to do it perhaps is to  just to get the VAD labels and the final features.


Yeah , the way we want to do it perhaps is to  just to get the VAD labels and the final features.

So they will send us the  Well , provide us with the feature files ,


So they will send us the  Well , provide us with the feature files ,

and with VAD uh , binary labels so that we can uh , get our MLP features and filter them with the VAD and then combine them with their f feature stream. So.


So they will send us the  Well , provide us with the feature files ,

and with VAD uh , binary labels so that we can uh , get our MLP features and filter them with the VAD and then combine them with their f feature stream. So.

I see. So we  So. 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. Without putting in a second path.


and with VAD uh , binary labels so that we can uh , get our MLP features and filter them with the VAD and then combine them with their f feature stream. So.

I see. So we  So. 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. Without putting in a second path.

Uh. You mean  Oh , yeah ! Just re re retraining r retraining the HTK ?


and with VAD uh , binary labels so that we can uh , get our MLP features and filter them with the VAD and then combine them with their f feature stream. So.

I see. So we  So. 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. Without putting in a second path.

Uh. You mean  Oh , yeah ! Just re re retraining r retraining the HTK ?

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.


I see. So we  So. 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. Without putting in a second path.

Uh. You mean  Oh , yeah ! Just re re retraining r retraining the HTK ?

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.


Uh. You mean  Oh , yeah ! Just re re retraining r retraining the HTK ?

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.


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.

Uh , I mean , I don't know that we need to r


Uh , I mean , I don't know that we need to r


Uh , I mean , I don't know that we need to r

Um  Do we need to retrain I mean we can just take the re their training files also. But.  But , uh just for the testing , jus just make sure that we get the same results  so we can duplicate it before we add in another


Um  Do we need to retrain I mean we can just take the re their training files also. But.  But , uh just for the testing , jus just make sure that we get the same results  so we can duplicate it before we add in another

Cuz otherwise , you know , we won't know what things mean.


Um  Do we need to retrain I mean we can just take the re their training files also. But.  But , uh just for the testing , jus just make sure that we get the same results  so we can duplicate it before we add in another

Cuz otherwise , you know , we won't know what things mean.

Oh , yeah. OK. And um. Yeah , so fff , LogRASTA , I don't know if we want to  We can try  networks with LogRASTA filtered features.


Cuz otherwise , you know , we won't know what things mean.

Oh , yeah. OK. And um. Yeah , so fff , LogRASTA , I don't know if we want to  We can try  networks with LogRASTA filtered features.


Oh , yeah. OK. And um. Yeah , so fff , LogRASTA , I don't know if we want to  We can try  networks with LogRASTA filtered features.

Oh ! You know , the other thing is when you say comb I 'm  I 'm sorry , I 'm interrupting.  that u Um , uh , when you 're talking about combining multiple features , um  Suppose we said , " OK , we 've got these different features and so forth , but PLP seems  pretty good. " If we take the approach that Mike did and have


Oh ! You know , the other thing is when you say comb I 'm  I 'm sorry , I 'm interrupting.  that u Um , uh , when you 're talking about combining multiple features , um  Suppose we said , " OK , we 've got these different features and so forth , but PLP seems  pretty good. " If we take the approach that Mike did and have


Oh ! You know , the other thing is when you say comb I 'm  I 'm sorry , I 'm interrupting.  that u Um , uh , when you 're talking about combining multiple features , um  Suppose we said , " OK , we 've got these different features and so forth , but PLP seems  pretty good. " If we take the approach that Mike did and have

I mean , one of the situations we have is we have these different conditions. We have different languages , we have different   different noises , Um  If we have some drastically different conditions and we just train up different M L Ps  with them.


Oh ! You know , the other thing is when you say comb I 'm  I 'm sorry , I 'm interrupting.  that u Um , uh , when you 're talking about combining multiple features , um  Suppose we said , " OK , we 've got these different features and so forth , but PLP seems  pretty good. " If we take the approach that Mike did and have

I mean , one of the situations we have is we have these different conditions. We have different languages , we have different   different noises , Um  If we have some drastically different conditions and we just train up different M L Ps  with them.

And put  put them together. What  what  What Mike found , for the reverberation case at least , I mean  I mean , who knows if it 'll work for these other ones. That you did have nice interpolative effects. 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 , uh , and then you fed the thing , uh something that was a modest amount of reverberation then you 'd get some result in between the two. So it was sort of  behaved reasonably. Is tha that a fair  Yeah.


I mean , one of the situations we have is we have these different conditions. We have different languages , we have different   different noises , Um  If we have some drastically different conditions and we just train up different M L Ps  with them.

And put  put them together. What  what  What Mike found , for the reverberation case at least , I mean  I mean , who knows if it 'll work for these other ones. That you did have nice interpolative effects. 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 , uh , and then you fed the thing , uh something that was a modest amount of reverberation then you 'd get some result in between the two. So it was sort of  behaved reasonably. Is tha that a fair  Yeah.

Yeah. So you  you think it 's perhaps better to have several M L Yeah but


And put  put them together. What  what  What Mike found , for the reverberation case at least , I mean  I mean , who knows if it 'll work for these other ones. That you did have nice interpolative effects. 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 , uh , and then you fed the thing , uh something that was a modest amount of reverberation then you 'd get some result in between the two. So it was sort of  behaved reasonably. Is tha that a fair  Yeah.

Yeah. So you  you think it 's perhaps better to have several M L Yeah but


Yeah. So you  you think it 's perhaps better to have several M L Yeah but

I see. Well , see , i oc You were doing some something that was  So maybe the analogy isn't quite right. You were doing something that was in way a little better behaved. You had reverb for a single variable which was re uh , uh , reverberation. 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. I mean , a million frames actually isn't that much. We have a modest amount of  of uh training data from a couple different conditions , and then uh  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 uh , and uh ,  uh , channel characteristic , sort of all over the map. A bunch of different dimensions. And so , I 'm just concerned that we don't really have  um , the data to train up  I mean 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 uh , a fe few different databases and uh the performance is getting worse and uh , when we just take one of those databases that 's a pretty good one , it actually is  is  is  is  is better. And uh 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 uh , seemingly , the ability  to represent , in the neural net of the size that we have , um , all of the variability  that we 're gonna be covering. So that I 'm  I 'm  I 'm hoping that um , this is another take on the efficiency argument you 're making , which is I 'm hoping that with moderate size neural nets , uh , that uh if we  if they look at more constrained conditions they  they 'll have enough parameters to really represent them. Mm - hmm. Mm - hmm. Mm - hmm. Yeah.


I see. Well , see , i oc You were doing some something that was  So maybe the analogy isn't quite right. You were doing something that was in way a little better behaved. You had reverb for a single variable which was re uh , uh , reverberation. 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. I mean , a million frames actually isn't that much. We have a modest amount of  of uh training data from a couple different conditions , and then uh  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 uh , and uh ,  uh , channel characteristic , sort of all over the map. A bunch of different dimensions. And so , I 'm just concerned that we don't really have  um , the data to train up  I mean 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 uh , a fe few different databases and uh the performance is getting worse and uh , when we just take one of those databases that 's a pretty good one , it actually is  is  is  is  is better. And uh 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 uh , seemingly , the ability  to represent , in the neural net of the size that we have , um , all of the variability  that we 're gonna be covering. So that I 'm  I 'm  I 'm hoping that um , this is another take on the efficiency argument you 're making , which is I 'm hoping that with moderate size neural nets , uh , that uh if we  if they look at more constrained conditions they  they 'll have enough parameters to really represent them. Mm - hmm. Mm - hmm. Mm - hmm. Yeah.

So doing both is  is not  is not right , you mean , or  ? Yeah.


So doing both is  is not  is not right , you mean , or  ? Yeah.

Yeah. I mean   i i e The um  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  I mean it 's done in the log domain , as I recall , and it 's  it uh  it 's just that they d it 's trained up , right ? That that um benefitted from on - line normalization. So they did  At least in their case , it did seem to be somewhat complimentary. So will it be in our case , where we 're using the neural net ? I mean they  they were not  not using the neural net. Uh I don't know. OK , so the other things you have here are uh , trying to improve results from a single  Yeah. Make stuff better. OK. Uh.  Yeah. And CPU memory issues. Yeah. We 've been sort of ignoring that , haven't we ?