Um. I mean , it seems to me , if you have exactly the right thing then it 's better to do it without the net because otherwise you 're asking the net to learn this  you know , say if you wanted to learn how to do multiplication.

I mean you could feed it a bunch of s you could feed two numbers that you wanted to multiply into a net and have a bunch of nonlinearities in the middle and train it to get the product of the output and it would work. But , it 's kind of crazy , cuz we know how to multiply and you  you 'd be you know much lower error usually  if you just multiplied it out. But suppose you don't really know what the right thing is. And that 's what these sort of dumb machine learning methods are good at. So. Um. Anyway. It 's just a thought.


Um. I mean , it seems to me , if you have exactly the right thing then it 's better to do it without the net because otherwise you 're asking the net to learn this  you know , say if you wanted to learn how to do multiplication.

I mean you could feed it a bunch of s you could feed two numbers that you wanted to multiply into a net and have a bunch of nonlinearities in the middle and train it to get the product of the output and it would work. But , it 's kind of crazy , cuz we know how to multiply and you  you 'd be you know much lower error usually  if you just multiplied it out. But suppose you don't really know what the right thing is. And that 's what these sort of dumb machine learning methods are good at. So. Um. Anyway. It 's just a thought.

How long does it take , Carmen , to train up one of these nets ?


I mean you could feed it a bunch of s you could feed two numbers that you wanted to multiply into a net and have a bunch of nonlinearities in the middle and train it to get the product of the output and it would work. But , it 's kind of crazy , cuz we know how to multiply and you  you 'd be you know much lower error usually  if you just multiplied it out. But suppose you don't really know what the right thing is. And that 's what these sort of dumb machine learning methods are good at. So. Um. Anyway. It 's just a thought.

How long does it take , Carmen , to train up one of these nets ?


How long does it take , Carmen , to train up one of these nets ?


What are  what are your f uh frame error rates for  for this ?


What are  what are your f uh frame error rates for  for this ?

Eh fifty - f six uh no , the frame error rate ?


Eh fifty - f six uh no , the frame error rate ?


Fif - fifty - six percent accurate for v voice - unvoice


Fif - fifty - six percent accurate for v voice - unvoice

The accuracy. Mm - hmm. No for , yes f I don't remember for voice - unvoice ,


The accuracy. Mm - hmm. No for , yes f I don't remember for voice - unvoice ,


Yeah , voiced - unvoiced hopefully would be a lot better.


This is for the other one. I should  I can't show that.


This is for the other one. I should  I can't show that.

But I think that fifty - five was for the  when the output are the fifty - six phone.


But I think that fifty - five was for the  when the output are the fifty - six phone.

That I look in the  with the other  nnn the other MLP that we have are more or less the same number. Silence will be better but more or less the same.


But I think that fifty - five was for the  when the output are the fifty - six phone.

That I look in the  with the other  nnn the other MLP that we have are more or less the same number. Silence will be better but more or less the same.

I think at the frame level for fifty - six that was the kind of number we were getting for  for uh um reduced band width uh stuff.


That I look in the  with the other  nnn the other MLP that we have are more or less the same number. Silence will be better but more or less the same.

I think at the frame level for fifty - six that was the kind of number we were getting for  for uh um reduced band width uh stuff.

I think that  I  I  I think that for the other one , for the three output , is sixty sixty - two , sixty three more or less.


I think at the frame level for fifty - six that was the kind of number we were getting for  for uh um reduced band width uh stuff.

I think that  I  I  I think that for the other one , for the three output , is sixty sixty - two , sixty three more or less.


I think that  I  I  I think that for the other one , for the three output , is sixty sixty - two , sixty three more or less.


But even i in  Oh yeah , in training. Still , Uh. Well actually , so this is a test that you should do then. Um , if you 're getting fifty - six percent over here , uh that 's in noise also , right ?


But even i in  Oh yeah , in training. Still , Uh. Well actually , so this is a test that you should do then. Um , if you 're getting fifty - six percent over here , uh that 's in noise also , right ?

Oh OK. If you 're getting fifty - six here , try adding together the probabilities of all of the voiced phones here and all of the unvoiced phones


Oh OK. If you 're getting fifty - six here , try adding together the probabilities of all of the voiced phones here and all of the unvoiced phones


I bet you get better than sixty - three.


I bet you get better than sixty - three.

Well I don't know , but  I th I  I think that we  I have the result more or less. Maybe. I don't know. I don't  I 'm not sure but I remember @ @ that I can't show that.


I bet you get better than sixty - three.

Well I don't know , but  I th I  I think that we  I have the result more or less. Maybe. I don't know. I don't  I 'm not sure but I remember @ @ that I can't show that.

OK , but that 's a  That is a  a good check point , you should do that anyway ,


Well I don't know , but  I th I  I think that we  I have the result more or less. Maybe. I don't know. I don't  I 'm not sure but I remember @ @ that I can't show that.

OK , but that 's a  That is a  a good check point , you should do that anyway ,

OK ? Given this  this uh regular old net that 's just for choosing for other purposes , uh add up the probabilities of the different subclasses and see  see how well you do. Uh and that  you know anything that you do over here should be at least as good as that.


OK , but that 's a  That is a  a good check point , you should do that anyway ,

OK ? Given this  this uh regular old net that 's just for choosing for other purposes , uh add up the probabilities of the different subclasses and see  see how well you do. Uh and that  you know anything that you do over here should be at least as good as that.


OK ? Given this  this uh regular old net that 's just for choosing for other purposes , uh add up the probabilities of the different subclasses and see  see how well you do. Uh and that  you know anything that you do over here should be at least as good as that.


The targets for the neural net , uh , they come from forced alignments ?


Noisy TIMIT. We have noisy TIMIT with the noise of the  the TI - digits. And now we have another noisy TIMIT also with the noise of uh Italian database.


Noisy TIMIT. We have noisy TIMIT with the noise of the  the TI - digits. And now we have another noisy TIMIT also with the noise of uh Italian database.

I see. Yeah. Well there 's gonna be  it looks like there 's gonna be a noisy uh  some large vocabulary noisy stuff too. Somebody 's preparing.


Noisy TIMIT. We have noisy TIMIT with the noise of the  the TI - digits. And now we have another noisy TIMIT also with the noise of uh Italian database.

I see. Yeah. Well there 's gonna be  it looks like there 's gonna be a noisy uh  some large vocabulary noisy stuff too. Somebody 's preparing.

Yeah. I forget what it 'll be , resource management , Wall Street Journal , something. Some  some read task actually , that they 're  preparing.


I see. Yeah. Well there 's gonna be  it looks like there 's gonna be a noisy uh  some large vocabulary noisy stuff too. Somebody 's preparing.

Yeah. I forget what it 'll be , resource management , Wall Street Journal , something. Some  some read task actually , that they 're  preparing.


Yeah. I forget what it 'll be , resource management , Wall Street Journal , something. Some  some read task actually , that they 're  preparing.


Yeah , so the uh  Uh , the issue is whether people make a decision now based on what they 've already seen , or they make it later. And one of the arguments for making it later is let 's make sure that whatever techniques that we 're using work for something more than  than connected digits.


Yeah , so the uh  Uh , the issue is whether people make a decision now based on what they 've already seen , or they make it later. And one of the arguments for making it later is let 's make sure that whatever techniques that we 're using work for something more than  than connected digits.

When are they planning  When would they do that ?


When are they planning  When would they do that ?

Mmm , I think late  uh I think in the summer sometime.


Mmm , I think late  uh I think in the summer sometime.

This is the work that I did during this date


This is the work that I did during this date


This is the work that I did during this date

and also mmm I  H Hynek last week say that if I have time I can to begin to  to study well seriously the France Telecom proposal


and also mmm I  H Hynek last week say that if I have time I can to begin to  to study well seriously the France Telecom proposal

to look at the code and something like that to know exactly what they are doing because maybe that we can have some ideas


to look at the code and something like that to know exactly what they are doing because maybe that we can have some ideas

but not only to read the proposal. Look insi look i carefully what they are doing with the program @ @ and I begin to  to work also in that. But the first thing that I don't understand is that they are using R - the uh log energy that this quite  I don't know why they have some constant in the expression of the lower energy. I don't know what that means.


to look at the code and something like that to know exactly what they are doing because maybe that we can have some ideas

but not only to read the proposal. Look insi look i carefully what they are doing with the program @ @ and I begin to  to work also in that. But the first thing that I don't understand is that they are using R - the uh log energy that this quite  I don't know why they have some constant in the expression of the lower energy. I don't know what that means.

They have a constant in there , you said ?


but not only to read the proposal. Look insi look i carefully what they are doing with the program @ @ and I begin to  to work also in that. But the first thing that I don't understand is that they are using R - the uh log energy that this quite  I don't know why they have some constant in the expression of the lower energy. I don't know what that means.

They have a constant in there , you said ?


but not only to read the proposal. Look insi look i carefully what they are doing with the program @ @ and I begin to  to work also in that. But the first thing that I don't understand is that they are using R - the uh log energy that this quite  I don't know why they have some constant in the expression of the lower energy. I don't know what that means.

They have a constant in there , you said ?

Oh , at the front it says uh " log energy is equal to the rounded version of sixteen over the log of two "


They have a constant in there , you said ?

Oh , at the front it says uh " log energy is equal to the rounded version of sixteen over the log of two "


Oh , at the front it says uh " log energy is equal to the rounded version of sixteen over the log of two "


Well , this is natural log , and maybe it has something to do with the fact that this is  I  I have no idea.


Well , this is natural log , and maybe it has something to do with the fact that this is  I  I have no idea.

Is that some kind of base conversion , or  ?


Well , this is natural log , and maybe it has something to do with the fact that this is  I  I have no idea.

Is that some kind of base conversion , or  ?

Yeah , that 's what I was thinking , but  but um , then there 's the sixty - four , Uh ,  I don't know.


Well , this is natural log , and maybe it has something to do with the fact that this is  I  I have no idea.

Is that some kind of base conversion , or  ?

Yeah , that 's what I was thinking , but  but um , then there 's the sixty - four , Uh ,  I don't know.

Because maybe they 're  the threshold that they are using on the basis of this value


Is that some kind of base conversion , or  ?

Yeah , that 's what I was thinking , but  but um , then there 's the sixty - four , Uh ,  I don't know.

Because maybe they 're  the threshold that they are using on the basis of this value


Yeah , that 's what I was thinking , but  but um , then there 's the sixty - four , Uh ,  I don't know.

Because maybe they 're  the threshold that they are using on the basis of this value


Because maybe they 're  the threshold that they are using on the basis of this value

I don't know exactly , because well th I thought maybe they have a meaning. But I don't know what is the meaning of take exactly this value.


I don't know exactly , because well th I thought maybe they have a meaning. But I don't know what is the meaning of take exactly this value.


I don't know exactly , because well th I thought maybe they have a meaning. But I don't know what is the meaning of take exactly this value.

So they 're taking the number inside the log and raising it to sixteen over log base two.


I don't know exactly , because well th I thought maybe they have a meaning. But I don't know what is the meaning of take exactly this value.

So they 're taking the number inside the log and raising it to sixteen over log base two.

I don't know. Yeah , I  um Right. Sixteen over  two.


So they 're taking the number inside the log and raising it to sixteen over log base two.

I don't know. Yeah , I  um Right. Sixteen over  two.

Does it have to do with those sixty - fours , or  ?


So they 're taking the number inside the log and raising it to sixteen over log base two.

I don't know. Yeah , I  um Right. Sixteen over  two.

Does it have to do with those sixty - fours , or  ?

Um. If we ignore the sixteen , the natural log of t one over the natural log of two times the natu I don't know. Well , maybe somebody 'll think of something ,


I don't know. Yeah , I  um Right. Sixteen over  two.

Does it have to do with those sixty - fours , or  ?

Um. If we ignore the sixteen , the natural log of t one over the natural log of two times the natu I don't know. Well , maybe somebody 'll think of something ,

but this is uh  It may just be that they  they want to have  for very small energies , they want to have some kind of a


Does it have to do with those sixty - fours , or  ?

Um. If we ignore the sixteen , the natural log of t one over the natural log of two times the natu I don't know. Well , maybe somebody 'll think of something ,

but this is uh  It may just be that they  they want to have  for very small energies , they want to have some kind of a

Yeah , the e The effect I don't  @ @ I can understand the effect of this , no ? because it 's to  to do something like that.


Um. If we ignore the sixteen , the natural log of t one over the natural log of two times the natu I don't know. Well , maybe somebody 'll think of something ,

but this is uh  It may just be that they  they want to have  for very small energies , they want to have some kind of a

Yeah , the e The effect I don't  @ @ I can understand the effect of this , no ? because it 's to  to do something like that.

Well , it says , since you 're taking a natural log , it says that when  when you get down to essentially zero energy , this is gonna be the natural log of one , which is zero.


but this is uh  It may just be that they  they want to have  for very small energies , they want to have some kind of a

Yeah , the e The effect I don't  @ @ I can understand the effect of this , no ? because it 's to  to do something like that.

Well , it says , since you 're taking a natural log , it says that when  when you get down to essentially zero energy , this is gonna be the natural log of one , which is zero.


Yeah , the e The effect I don't  @ @ I can understand the effect of this , no ? because it 's to  to do something like that.

Well , it says , since you 're taking a natural log , it says that when  when you get down to essentially zero energy , this is gonna be the natural log of one , which is zero.

So it 'll go down to uh to  the natural log being  So the lowest value for this would be zero. So y you 're restricted to being positive. And this sort of smooths it for very small energies. Uh , why they chose sixty - four and something else , that was probably just experimental. And the  the  the constant in front of it , I have no idea.


Well , it says , since you 're taking a natural log , it says that when  when you get down to essentially zero energy , this is gonna be the natural log of one , which is zero.

So it 'll go down to uh to  the natural log being  So the lowest value for this would be zero. So y you 're restricted to being positive. And this sort of smooths it for very small energies. Uh , why they chose sixty - four and something else , that was probably just experimental. And the  the  the constant in front of it , I have no idea.


So it 'll go down to uh to  the natural log being  So the lowest value for this would be zero. So y you 're restricted to being positive. And this sort of smooths it for very small energies. Uh , why they chose sixty - four and something else , that was probably just experimental. And the  the  the constant in front of it , I have no idea.


So it 'll go down to uh to  the natural log being  So the lowest value for this would be zero. So y you 're restricted to being positive. And this sort of smooths it for very small energies. Uh , why they chose sixty - four and something else , that was probably just experimental. And the  the  the constant in front of it , I have no idea.

Well. I  I will look to try if I move this parameter in their code what happens , maybe everything is  Maybe they tres hole are on basis of this.


Well. I  I will look to try if I move this parameter in their code what happens , maybe everything is  Maybe they tres hole are on basis of this.

uh  I mean  it   they  they probably have some fi particular s fixed point arithmetic that they 're using ,


uh  I mean  it   they  they probably have some fi particular s fixed point arithmetic that they 're using ,

Yeah , I was just gonna say maybe it has something to do with hardware ,


Yeah , I was just gonna say maybe it has something to do with hardware ,


Yeah , I was just gonna say maybe it has something to do with hardware ,

Yeah , I mean that  they 're s probably working with fixed point or integer or something. I think you 're supposed to on this stuff anyway , and  and so maybe that puts it in the right realm somewhere.


Yeah , I mean that  they 're s probably working with fixed point or integer or something. I think you 're supposed to on this stuff anyway , and  and so maybe that puts it in the right realm somewhere.

Well it just , yeah , puts it in the right range , or


Yeah , I mean that  they 're s probably working with fixed point or integer or something. I think you 're supposed to on this stuff anyway , and  and so maybe that puts it in the right realm somewhere.

Well it just , yeah , puts it in the right range , or

Yeah. I think , given at the level you 're doing things in floating point on the computer , I don't think it matters , would be my guess ,


Well it just , yeah , puts it in the right range , or

Yeah. I think , given at the level you 're doing things in floating point on the computer , I don't think it matters , would be my guess ,


Yeah. I think , given at the level you 're doing things in floating point on the computer , I don't think it matters , would be my guess ,


Yeah. OK , and wh when did Stephane take off ? He took off


Yeah. OK , and wh when did Stephane take off ? He took off

I think that Stephane will arrive today or tomorrow.


Yeah. OK , and wh when did Stephane take off ? He took off

I think that Stephane will arrive today or tomorrow.

Oh , he was gone these first few days , and then he 's here for a couple days before he goes to Salt Lake City.


I think that Stephane will arrive today or tomorrow.

Oh , he was gone these first few days , and then he 's here for a couple days before he goes to Salt Lake City.


Oh , he was gone these first few days , and then he 's here for a couple days before he goes to Salt Lake City.

He 's  I think that he is in Las Vegas or something like that.


He 's  I think that he is in Las Vegas or something like that.

Yeah. Yeah. So he 's  he 's going to ICASSP which is good. I  I don't know if there are many people who are going to ICASSP


He 's  I think that he is in Las Vegas or something like that.

Yeah. Yeah. So he 's  he 's going to ICASSP which is good. I  I don't know if there are many people who are going to ICASSP

so  so I thought , make sure somebody go.


Yeah. Yeah. So he 's  he 's going to ICASSP which is good. I  I don't know if there are many people who are going to ICASSP

so  so I thought , make sure somebody go.


so  so I thought , make sure somebody go.

Do  have  Have people sort of stopped going to ICASSP in recent years ?


so  so I thought , make sure somebody go.

Do  have  Have people sort of stopped going to ICASSP in recent years ?

Um , people are less consistent about going to ICASSP and I think it 's still  it 's still a reasonable forum for students to  to present things. Uh , it 's  I think for engineering students of any kind , I think it 's  it 's if you haven't been there much , it 's good to go to , uh to get a feel for things , a range of things , not just speech. Uh. But I think for  for sort of dyed - in - the - wool speech people , um I think that ICSLP and Eurospeech are much more targeted.


Do  have  Have people sort of stopped going to ICASSP in recent years ?

Um , people are less consistent about going to ICASSP and I think it 's still  it 's still a reasonable forum for students to  to present things. Uh , it 's  I think for engineering students of any kind , I think it 's  it 's if you haven't been there much , it 's good to go to , uh to get a feel for things , a range of things , not just speech. Uh. But I think for  for sort of dyed - in - the - wool speech people , um I think that ICSLP and Eurospeech are much more targeted.


Do  have  Have people sort of stopped going to ICASSP in recent years ?

Um , people are less consistent about going to ICASSP and I think it 's still  it 's still a reasonable forum for students to  to present things. Uh , it 's  I think for engineering students of any kind , I think it 's  it 's if you haven't been there much , it 's good to go to , uh to get a feel for things , a range of things , not just speech. Uh. But I think for  for sort of dyed - in - the - wool speech people , um I think that ICSLP and Eurospeech are much more targeted.

Uh. And then there 's these other meetings , like HLT and  and uh ASRU


Um , people are less consistent about going to ICASSP and I think it 's still  it 's still a reasonable forum for students to  to present things. Uh , it 's  I think for engineering students of any kind , I think it 's  it 's if you haven't been there much , it 's good to go to , uh to get a feel for things , a range of things , not just speech. Uh. But I think for  for sort of dyed - in - the - wool speech people , um I think that ICSLP and Eurospeech are much more targeted.

Uh. And then there 's these other meetings , like HLT and  and uh ASRU

so there 's  there 's actually plenty of meetings that are really relevant to  to uh computational uh speech processing of one sort or another.


Uh. And then there 's these other meetings , like HLT and  and uh ASRU

so there 's  there 's actually plenty of meetings that are really relevant to  to uh computational uh speech processing of one sort or another.


Uh. And then there 's these other meetings , like HLT and  and uh ASRU

so there 's  there 's actually plenty of meetings that are really relevant to  to uh computational uh speech processing of one sort or another.

Um. So. I mean , I mostly just ignored it because I was too busy and  didn't get to it. So uh Wanna talk a little bit about what we were talking about this morning ?


so there 's  there 's actually plenty of meetings that are really relevant to  to uh computational uh speech processing of one sort or another.

Um. So. I mean , I mostly just ignored it because I was too busy and  didn't get to it. So uh Wanna talk a little bit about what we were talking about this morning ?


Um. So. I mean , I mostly just ignored it because I was too busy and  didn't get to it. So uh Wanna talk a little bit about what we were talking about this morning ?


Um. So. I mean , I mostly just ignored it because I was too busy and  didn't get to it. So uh Wanna talk a little bit about what we were talking about this morning ?

So. I  I guess some of the progress , I  I 've been getting a  getting my committee members for the quals. And um so far I have Morgan and Hynek ,  Mike Jordan , and I asked John Ohala and he agreed. Yeah. Yeah.


So. I  I guess some of the progress , I  I 've been getting a  getting my committee members for the quals. And um so far I have Morgan and Hynek ,  Mike Jordan , and I asked John Ohala and he agreed. Yeah. Yeah.


So. I  I guess some of the progress , I  I 've been getting a  getting my committee members for the quals. And um so far I have Morgan and Hynek ,  Mike Jordan , and I asked John Ohala and he agreed. Yeah. Yeah.

So I 'm  I  I just need to ask um Malek. One more. Um. Tsk. Then uh I talked a little bit about  um continuing with these dynamic ev um acoustic events , and um   we 're  we 're  we 're  thinking about a way to test the completeness of a  a set of um dynamic uh events. Uh , completeness in the  in the sense that  um if we  if we pick these X number of acoustic events ,  do they provide sufficient coverage  for the phones that we 're trying to recognize  or  or the f the words that we 're gonna try to recognize later on. And so Morgan and I were uh discussing  um s uh s a form of a cheating experiment  where we get   um we have uh  um a chosen set of features , or acoustic events , and we train up a hybrid  um system to do phone recognition on TIMIT. So i i the idea is if we get good phone recognition results ,  using um these set of acoustic events ,  then  um that  that says that these acoustic events are g sufficient to cover  a set of phones , at least found in TIMIT. Um so i it would be a   a measure of " are we on the right track with  with the  the choices of our acoustic events ". Um ,  So that 's going on. And  also , just uh working on my  uh final project for Jordan 's class , uh which is


So I 'm  I  I just need to ask um Malek. One more. Um. Tsk. Then uh I talked a little bit about  um continuing with these dynamic ev um acoustic events , and um   we 're  we 're  we 're  thinking about a way to test the completeness of a  a set of um dynamic uh events. Uh , completeness in the  in the sense that  um if we  if we pick these X number of acoustic events ,  do they provide sufficient coverage  for the phones that we 're trying to recognize  or  or the f the words that we 're gonna try to recognize later on. And so Morgan and I were uh discussing  um s uh s a form of a cheating experiment  where we get   um we have uh  um a chosen set of features , or acoustic events , and we train up a hybrid  um system to do phone recognition on TIMIT. So i i the idea is if we get good phone recognition results ,  using um these set of acoustic events ,  then  um that  that says that these acoustic events are g sufficient to cover  a set of phones , at least found in TIMIT. Um so i it would be a   a measure of " are we on the right track with  with the  the choices of our acoustic events ". Um ,  So that 's going on. And  also , just uh working on my  uh final project for Jordan 's class , uh which is


Let me back up while we 're still on it. The  the other thing I was suggesting , though , is that given that you 're talking about binary features , uh , maybe the first thing to do is just to count and uh count co - occurrences and get probabilities for a discrete HMM cuz that 'd be pretty simple because it 's just  Say , if you had ten  ten events , uh that you were counting , uh each frame would only have a thousand possible values for these ten bits , and uh so you could make a table that would  say , if you had thirty - nine phone categories , that would be a thousand by thirty - nine , and just count the co - occurrences and divide them by the  the uh  uh uh occ uh count the co - occurrences between the event and the phone and divide them by the number of occurrences of the phone , and that would give you the likelihood of the  of the event given the phone. And um then just use that in a very simple HMM and uh you could uh do phone recognition then and uh wouldn't have any of the issues of the uh training of the net or  I mean , it 'd be on the simple side , but


Let me back up while we 're still on it. The  the other thing I was suggesting , though , is that given that you 're talking about binary features , uh , maybe the first thing to do is just to count and uh count co - occurrences and get probabilities for a discrete HMM cuz that 'd be pretty simple because it 's just  Say , if you had ten  ten events , uh that you were counting , uh each frame would only have a thousand possible values for these ten bits , and uh so you could make a table that would  say , if you had thirty - nine phone categories , that would be a thousand by thirty - nine , and just count the co - occurrences and divide them by the  the uh  uh uh occ uh count the co - occurrences between the event and the phone and divide them by the number of occurrences of the phone , and that would give you the likelihood of the  of the event given the phone. And um then just use that in a very simple HMM and uh you could uh do phone recognition then and uh wouldn't have any of the issues of the uh training of the net or  I mean , it 'd be on the simple side , but

uh um you know , if  uh uh the example I was giving was that if  if you had um onset of voicing and  and end of voicing as being two kinds of events , then if you had those a all marked correctly , and you counted co - occurrences , you should get it completely right.


uh um you know , if  uh uh the example I was giving was that if  if you had um onset of voicing and  and end of voicing as being two kinds of events , then if you had those a all marked correctly , and you counted co - occurrences , you should get it completely right.

So. um  But you 'd get all the other distinctions , you know , randomly wrong. I mean there 'd be nothing to tell you that. So um  uh If you just do this by counting , then you should be able to find out in a pretty straightforward way whether you have a sufficient uh set of events to  to do the kind of level of   of uh classification of phones that you 'd like. So that was  that was the idea. And then the other thing that we were discussing was  was um  OK , how do you get the  your training data.


So. um  But you 'd get all the other distinctions , you know , randomly wrong. I mean there 'd be nothing to tell you that. So um  uh If you just do this by counting , then you should be able to find out in a pretty straightforward way whether you have a sufficient uh set of events to  to do the kind of level of   of uh classification of phones that you 'd like. So that was  that was the idea. And then the other thing that we were discussing was  was um  OK , how do you get the  your training data.

Cuz uh the  Switchboard transcription project uh uh you know was half a dozen people , or so working off and on over a couple years , and uh similar   similar amount of data  to what you 're talking about with TIMIT training. So , it seems to me that the only reasonable starting point is uh to automatically translate the uh current TIMIT markings into the markings you want. And uh  it won't have the kind of characteristic that you 'd like , of catching funny kind of things that maybe aren't there from these automatic markings ,


Cuz uh the  Switchboard transcription project uh uh you know was half a dozen people , or so working off and on over a couple years , and uh similar   similar amount of data  to what you 're talking about with TIMIT training. So , it seems to me that the only reasonable starting point is uh to automatically translate the uh current TIMIT markings into the markings you want. And uh  it won't have the kind of characteristic that you 'd like , of catching funny kind of things that maybe aren't there from these automatic markings ,


Yeah and a short  short amount of time , just to  again , just to see if that information is sufficient to uh determine the phones.


Yeah and a short  short amount of time , just to  again , just to see if that information is sufficient to uh determine the phones.

Yeah , you could even then  to  to get an idea about how different it is , you could maybe take some subset and you know , go through a few sentences , mark them by hand and then see how different it is from you know , the canonical ones ,


Yeah , you could even then  to  to get an idea about how different it is , you could maybe take some subset and you know , go through a few sentences , mark them by hand and then see how different it is from you know , the canonical ones ,


Yeah , you could even then  to  to get an idea about how different it is , you could maybe take some subset and you know , go through a few sentences , mark them by hand and then see how different it is from you know , the canonical ones ,

just to get an idea  a rough idea of h if it really even makes a difference.


Yeah , you could even then  to  to get an idea about how different it is , you could maybe take some subset and you know , go through a few sentences , mark them by hand and then see how different it is from you know , the canonical ones ,

just to get an idea  a rough idea of h if it really even makes a difference.

You can get a little feeling for it that way , yeah that is probably right.


just to get an idea  a rough idea of h if it really even makes a difference.

You can get a little feeling for it that way , yeah that is probably right.


just to get an idea  a rough idea of h if it really even makes a difference.

You can get a little feeling for it that way , yeah that is probably right.

I mean uh my  my guess would be that this is  since TIMIT 's read speech that this would be less of a big deal ,


You can get a little feeling for it that way , yeah that is probably right.

I mean uh my  my guess would be that this is  since TIMIT 's read speech that this would be less of a big deal ,


I mean uh my  my guess would be that this is  since TIMIT 's read speech that this would be less of a big deal ,

if you went and looked at spontaneous speech it 'd be more  more of one.


if you went and looked at spontaneous speech it 'd be more  more of one.

And the other thing would be , say , if you had these ten events , you 'd wanna see , well what if you took two events or four events or ten events or t and you know , and  and hopefully there should be some point at which  having more information doesn't tell you really all that much more about what the phones are.


if you went and looked at spontaneous speech it 'd be more  more of one.

And the other thing would be , say , if you had these ten events , you 'd wanna see , well what if you took two events or four events or ten events or t and you know , and  and hopefully there should be some point at which  having more information doesn't tell you really all that much more about what the phones are.

Mm - hmm. You could define other events as being sequences of these events too.


And the other thing would be , say , if you had these ten events , you 'd wanna see , well what if you took two events or four events or ten events or t and you know , and  and hopefully there should be some point at which  having more information doesn't tell you really all that much more about what the phones are.

Mm - hmm. You could define other events as being sequences of these events too.

Uh , you could , but the thing is , what he 's talking about here is a uh  a translation to a per - frame feature vector , so there 's no sequence in that , I think. I think it 's just a


And the other thing would be , say , if you had these ten events , you 'd wanna see , well what if you took two events or four events or ten events or t and you know , and  and hopefully there should be some point at which  having more information doesn't tell you really all that much more about what the phones are.

Mm - hmm. You could define other events as being sequences of these events too.

Uh , you could , but the thing is , what he 's talking about here is a uh  a translation to a per - frame feature vector , so there 's no sequence in that , I think. I think it 's just a

Unless you did like a second pass over it or something after you 've got your


Mm - hmm. You could define other events as being sequences of these events too.

Uh , you could , but the thing is , what he 's talking about here is a uh  a translation to a per - frame feature vector , so there 's no sequence in that , I think. I think it 's just a

Unless you did like a second pass over it or something after you 've got your

Yeah , but we 're just talking about something simple here , yeah , to see if


Uh , you could , but the thing is , what he 's talking about here is a uh  a translation to a per - frame feature vector , so there 's no sequence in that , I think. I think it 's just a

Unless you did like a second pass over it or something after you 've got your

Yeah , but we 're just talking about something simple here , yeah , to see if

Yeah. Yeah , yeah. Yeah. I 'm adding complexity.


Unless you did like a second pass over it or something after you 've got your

Yeah , but we 're just talking about something simple here , yeah , to see if

Yeah. Yeah , yeah. Yeah. I 'm adding complexity.

Yeah. Just  You know. The idea is with a  with a very simple statistical structure , could you  could you uh at least verify that you 've chosen features that  are sufficient.


Yeah , but we 're just talking about something simple here , yeah , to see if

Yeah. Yeah , yeah. Yeah. I 'm adding complexity.

Yeah. Just  You know. The idea is with a  with a very simple statistical structure , could you  could you uh at least verify that you 've chosen features that  are sufficient.


Yeah. Yeah , yeah. Yeah. I 'm adding complexity.

Yeah. Just  You know. The idea is with a  with a very simple statistical structure , could you  could you uh at least verify that you 've chosen features that  are sufficient.

OK , and you were saying something  starting to say something else about your  your class project , or  ?


Yeah. Just  You know. The idea is with a  with a very simple statistical structure , could you  could you uh at least verify that you 've chosen features that  are sufficient.

OK , and you were saying something  starting to say something else about your  your class project , or  ?


OK , and you were saying something  starting to say something else about your  your class project , or  ?


OK , and you were saying something  starting to say something else about your  your class project , or  ?

So for my class project I 'm  um   I 'm tinkering with uh support vector machines ? something that we learned in class , and uh um basically just another method for doing classification. And so I 'm gonna apply that to  um compare it with the results by um King and Taylor who did  um these um using recurrent neural nets , they recognized  um  a set of phonological features um and made a mapping from the MFCC 's to these phonological features , so I 'm gonna  do a similar thing with   with support vector machines and see if


So for my class project I 'm  um   I 'm tinkering with uh support vector machines ? something that we learned in class , and uh um basically just another method for doing classification. And so I 'm gonna apply that to  um compare it with the results by um King and Taylor who did  um these um using recurrent neural nets , they recognized  um  a set of phonological features um and made a mapping from the MFCC 's to these phonological features , so I 'm gonna  do a similar thing with   with support vector machines and see if

So what 's the advantage of support vector machines ? What


So for my class project I 'm  um   I 'm tinkering with uh support vector machines ? something that we learned in class , and uh um basically just another method for doing classification. And so I 'm gonna apply that to  um compare it with the results by um King and Taylor who did  um these um using recurrent neural nets , they recognized  um  a set of phonological features um and made a mapping from the MFCC 's to these phonological features , so I 'm gonna  do a similar thing with   with support vector machines and see if

So what 's the advantage of support vector machines ? What

Um. So , support vector machines are  are good with dealing with a less amount of data


So what 's the advantage of support vector machines ? What

Um. So , support vector machines are  are good with dealing with a less amount of data

and um so if you  if you give it less data it still does a reasonable job  in learning the  the patterns.


Um. So , support vector machines are  are good with dealing with a less amount of data

and um so if you  if you give it less data it still does a reasonable job  in learning the  the patterns.


and um so if you  if you give it less data it still does a reasonable job  in learning the  the patterns.


and um so if you  if you give it less data it still does a reasonable job  in learning the  the patterns.

I guess it  yeah , they 're sort of succinct , and  and they  uh


I guess it  yeah , they 're sort of succinct , and  and they  uh


I guess it  yeah , they 're sort of succinct , and  and they  uh

Does there some kind of a distance metric that they use or how do they  for cla what do they do for classification ?


I guess it  yeah , they 're sort of succinct , and  and they  uh

Does there some kind of a distance metric that they use or how do they  for cla what do they do for classification ?

Um. Right. So ,  the  the simple idea behind a support vector machine is  um ,  you have  you have this feature space , right ? and then it finds the optimal separating plane , um between these two different um classes ,


Does there some kind of a distance metric that they use or how do they  for cla what do they do for classification ?

Um. Right. So ,  the  the simple idea behind a support vector machine is  um ,  you have  you have this feature space , right ? and then it finds the optimal separating plane , um between these two different um classes ,


Does there some kind of a distance metric that they use or how do they  for cla what do they do for classification ?

Um. Right. So ,  the  the simple idea behind a support vector machine is  um ,  you have  you have this feature space , right ? and then it finds the optimal separating plane , um between these two different um classes ,

and um  and so  um , what it  i at the end of the day , what it actually does is  it picks  those examples of the features that are closest to the separating boundary , and remembers those


Um. Right. So ,  the  the simple idea behind a support vector machine is  um ,  you have  you have this feature space , right ? and then it finds the optimal separating plane , um between these two different um classes ,

and um  and so  um , what it  i at the end of the day , what it actually does is  it picks  those examples of the features that are closest to the separating boundary , and remembers those


and um  and so  um , what it  i at the end of the day , what it actually does is  it picks  those examples of the features that are closest to the separating boundary , and remembers those

and   and uses them to recreate the boundary for the test set. So , given these  um these features , or  or these  these examples ,  um ,  critical examples ,  which they call support f support vectors ,  then um  given a new example ,  if the new example falls  um away from the boundary in one direction then it 's classified as being a part of this particular class


and   and uses them to recreate the boundary for the test set. So , given these  um these features , or  or these  these examples ,  um ,  critical examples ,  which they call support f support vectors ,  then um  given a new example ,  if the new example falls  um away from the boundary in one direction then it 's classified as being a part of this particular class


and   and uses them to recreate the boundary for the test set. So , given these  um these features , or  or these  these examples ,  um ,  critical examples ,  which they call support f support vectors ,  then um  given a new example ,  if the new example falls  um away from the boundary in one direction then it 's classified as being a part of this particular class

So why save the examples ? Why not just save what the boundary itself is ?


So why save the examples ? Why not just save what the boundary itself is ?

Mm - hmm. Um. Hmm. Let 's see. Uh. Yeah , that 's a good question. I  yeah.


So why save the examples ? Why not just save what the boundary itself is ?

Mm - hmm. Um. Hmm. Let 's see. Uh. Yeah , that 's a good question. I  yeah.

That 's another way of doing it. Right ? So  so it  I mean I  I guess it 's


Mm - hmm. Um. Hmm. Let 's see. Uh. Yeah , that 's a good question. I  yeah.

That 's another way of doing it. Right ? So  so it  I mean I  I guess it 's

You know , it  it goes back to nearest - neighbor  sort of thing ,


That 's another way of doing it. Right ? So  so it  I mean I  I guess it 's

You know , it  it goes back to nearest - neighbor  sort of thing ,


You know , it  it goes back to nearest - neighbor  sort of thing ,

right ? Um , i i if  is it eh w When is nearest - neighbor good ? Well , nearest - neighbor good  is good if you have lots and lots of examples. Um but of course if you have lots and lots of examples , then it can take a while to  to use nearest - neighbor. There 's lots of look ups. So a long time ago people talked about things where you would have uh a condensed nearest - neighbor , where you would  you would  you would pick out uh some representative examples which would uh be sufficient to represent  to  to correctly classify everything that came in.


right ? Um , i i if  is it eh w When is nearest - neighbor good ? Well , nearest - neighbor good  is good if you have lots and lots of examples. Um but of course if you have lots and lots of examples , then it can take a while to  to use nearest - neighbor. There 's lots of look ups. So a long time ago people talked about things where you would have uh a condensed nearest - neighbor , where you would  you would  you would pick out uh some representative examples which would uh be sufficient to represent  to  to correctly classify everything that came in.

I  I think s I think support vector stuff sort of goes back to   to that kind of thing. Um.


right ? Um , i i if  is it eh w When is nearest - neighbor good ? Well , nearest - neighbor good  is good if you have lots and lots of examples. Um but of course if you have lots and lots of examples , then it can take a while to  to use nearest - neighbor. There 's lots of look ups. So a long time ago people talked about things where you would have uh a condensed nearest - neighbor , where you would  you would  you would pick out uh some representative examples which would uh be sufficient to represent  to  to correctly classify everything that came in.

I  I think s I think support vector stuff sort of goes back to   to that kind of thing. Um.

I see. So rather than doing nearest neighbor where you compare to every single one , you just pick a few critical ones , and


I  I think s I think support vector stuff sort of goes back to   to that kind of thing. Um.

I see. So rather than doing nearest neighbor where you compare to every single one , you just pick a few critical ones , and


I see. So rather than doing nearest neighbor where you compare to every single one , you just pick a few critical ones , and

And th the You know , um neural net approach uh or Gaussian mixtures for that matter are sort of  fairly brute force kinds of things , where you sort of   you predefine that there is this big bunch of parameters and then you  you place them as you best can to define the boundaries , and in fact , as you know ,  these things do take a lot of parameters and  and uh  if you have uh only a modest amount of data , you have trouble  uh learning them. Um , so I  I guess the idea to this is that it  it is reputed to uh be somewhat better in that regard.


And th the You know , um neural net approach uh or Gaussian mixtures for that matter are sort of  fairly brute force kinds of things , where you sort of   you predefine that there is this big bunch of parameters and then you  you place them as you best can to define the boundaries , and in fact , as you know ,  these things do take a lot of parameters and  and uh  if you have uh only a modest amount of data , you have trouble  uh learning them. Um , so I  I guess the idea to this is that it  it is reputed to uh be somewhat better in that regard.


And th the You know , um neural net approach uh or Gaussian mixtures for that matter are sort of  fairly brute force kinds of things , where you sort of   you predefine that there is this big bunch of parameters and then you  you place them as you best can to define the boundaries , and in fact , as you know ,  these things do take a lot of parameters and  and uh  if you have uh only a modest amount of data , you have trouble  uh learning them. Um , so I  I guess the idea to this is that it  it is reputed to uh be somewhat better in that regard.

Right. I it can be a  a reduced um  parameterization of  of the  the model by just keeping  certain selected examples.


Right. I it can be a  a reduced um  parameterization of  of the  the model by just keeping  certain selected examples.


Right. I it can be a  a reduced um  parameterization of  of the  the model by just keeping  certain selected examples.

But I don't know if people have done sort of careful comparisons of this on large tasks or anything. Maybe  maybe they have. I don't know.


But I don't know if people have done sort of careful comparisons of this on large tasks or anything. Maybe  maybe they have. I don't know.


But I don't know if people have done sort of careful comparisons of this on large tasks or anything. Maybe  maybe they have. I don't know.

S do you get some kind of number between zero and one at the output ?


S do you get some kind of number between zero and one at the output ?

Actually you don't get a  you don't get a nice number between zero and one. You get  you get either a zero or a one. Um , uh there are  there are pap Well , basically , it 's  it 's um  you  you get a distance measure at the end of the day , and then that distance measure is  is um   is translated to a zero or one. Um.


S do you get some kind of number between zero and one at the output ?

Actually you don't get a  you don't get a nice number between zero and one. You get  you get either a zero or a one. Um , uh there are  there are pap Well , basically , it 's  it 's um  you  you get a distance measure at the end of the day , and then that distance measure is  is um   is translated to a zero or one. Um.

But that 's looking at it for  for classification  for binary classification ,


Actually you don't get a  you don't get a nice number between zero and one. You get  you get either a zero or a one. Um , uh there are  there are pap Well , basically , it 's  it 's um  you  you get a distance measure at the end of the day , and then that distance measure is  is um   is translated to a zero or one. Um.

But that 's looking at it for  for classification  for binary classification ,


But that 's looking at it for  for classification  for binary classification ,

And you get that for each class , you get a zero or a one.


And you get that for each class , you get a zero or a one.


And you get that for each class , you get a zero or a one.

But you have the distances to work with.


But you have the distances to work with.

Cuz actually Mississippi State people did use support vector machines for uh uh speech recognition and they were using it to estimate probabilities.


But you have the distances to work with.

Cuz actually Mississippi State people did use support vector machines for uh uh speech recognition and they were using it to estimate probabilities.

yeah. Yeah. Yeah , they   they had a  had a way to translate the distances into  into probabilities with the  with the simple  um  uh sigmoidal function.


Cuz actually Mississippi State people did use support vector machines for uh uh speech recognition and they were using it to estimate probabilities.

yeah. Yeah. Yeah , they   they had a  had a way to translate the distances into  into probabilities with the  with the simple  um  uh sigmoidal function.

Yeah , and d did they use sigmoid or a softmax type thing ?


yeah. Yeah. Yeah , they   they had a  had a way to translate the distances into  into probabilities with the  with the simple  um  uh sigmoidal function.

Yeah , and d did they use sigmoid or a softmax type thing ?

And didn't they like exponentiate or something


Yeah , and d did they use sigmoid or a softmax type thing ?

And didn't they like exponentiate or something

there 's some  there 's like one over one plus the exponential or something like that.


And didn't they like exponentiate or something

there 's some  there 's like one over one plus the exponential or something like that.

and then  divide by the sum of them , or  ? Oh it  i Oh , so it is a sigmoidal.


there 's some  there 's like one over one plus the exponential or something like that.

and then  divide by the sum of them , or  ? Oh it  i Oh , so it is a sigmoidal.


and then  divide by the sum of them , or  ? Oh it  i Oh , so it is a sigmoidal.

Did the  did they get good results with that ?


Did the  did they get good results with that ?

I mean , they 're OK , I  I don't  I don't think they were earth  earth shattering , but I think that  uh this was a couple years ago ,


Did the  did they get good results with that ?

I mean , they 're OK , I  I don't  I don't think they were earth  earth shattering , but I think that  uh this was a couple years ago ,

I remember them doing it at some meeting , and  and um I don't think people were very critical because it was interesting just to  to try this and you know , it was the first time they tried it , so   so the  you know , the numbers were not incredibly good


I mean , they 're OK , I  I don't  I don't think they were earth  earth shattering , but I think that  uh this was a couple years ago ,

I remember them doing it at some meeting , and  and um I don't think people were very critical because it was interesting just to  to try this and you know , it was the first time they tried it , so   so the  you know , the numbers were not incredibly good


I remember them doing it at some meeting , and  and um I don't think people were very critical because it was interesting just to  to try this and you know , it was the first time they tried it , so   so the  you know , the numbers were not incredibly good

but there 's you know , it was th reasonable.


but there 's you know , it was th reasonable.

I  I don't remember anymore. I don't even remember what the task was , it  was Broadcast News , or  something. I don't know.


I  I don't remember anymore. I don't even remember what the task was , it  was Broadcast News , or  something. I don't know.


I  I don't remember anymore. I don't even remember what the task was , it  was Broadcast News , or  something. I don't know.

Uh s So Barry , if you just have zero and ones , how are you doing the speech recognition ?


Uh s So Barry , if you just have zero and ones , how are you doing the speech recognition ?

Oh I 'm not do I 'm not planning on doing speech recognition with it. I 'm just doing  detection of phonological features.


Uh s So Barry , if you just have zero and ones , how are you doing the speech recognition ?

Oh I 'm not do I 'm not planning on doing speech recognition with it. I 'm just doing  detection of phonological features.

So uh for example ,  this  this uh feature set called the uh sound patterns of English  um is just a bunch of  um  binary valued features. Let 's say , is this voicing , or is this not voicing , is this  sonorants , not sonorants , and  stuff like that.


Oh I 'm not do I 'm not planning on doing speech recognition with it. I 'm just doing  detection of phonological features.

So uh for example ,  this  this uh feature set called the uh sound patterns of English  um is just a bunch of  um  binary valued features. Let 's say , is this voicing , or is this not voicing , is this  sonorants , not sonorants , and  stuff like that.


So uh for example ,  this  this uh feature set called the uh sound patterns of English  um is just a bunch of  um  binary valued features. Let 's say , is this voicing , or is this not voicing , is this  sonorants , not sonorants , and  stuff like that.


So uh for example ,  this  this uh feature set called the uh sound patterns of English  um is just a bunch of  um  binary valued features. Let 's say , is this voicing , or is this not voicing , is this  sonorants , not sonorants , and  stuff like that.

Did you find any more mistakes in their tables ?


Did you find any more mistakes in their tables ?

Oh ! Uh I haven't gone through the entire table ,  yet. Yeah , yesterday I brought Chuck  the table and I was like , " wait , this  is  Is the mapping from N to  to this phonological feature called um " coronal " , is  is  should it be  shouldn't it be a one ? or should it  should it be you know coronal instead of not coronal as it was labelled in the paper ? " So I ha haven't hunted down all the  all the mistakes yet ,


Oh ! Uh I haven't gone through the entire table ,  yet. Yeah , yesterday I brought Chuck  the table and I was like , " wait , this  is  Is the mapping from N to  to this phonological feature called um " coronal " , is  is  should it be  shouldn't it be a one ? or should it  should it be you know coronal instead of not coronal as it was labelled in the paper ? " So I ha haven't hunted down all the  all the mistakes yet ,

But a as I was saying , people do get probabilities from these things ,


But a as I was saying , people do get probabilities from these things ,


But a as I was saying , people do get probabilities from these things ,

and  and uh we were just trying to remember how they do , but people have used it for speech recognition , and they have gotten probabilities. So they have some conversion from these distances to probabilities.


and  and uh we were just trying to remember how they do , but people have used it for speech recognition , and they have gotten probabilities. So they have some conversion from these distances to probabilities.


and  and uh we were just trying to remember how they do , but people have used it for speech recognition , and they have gotten probabilities. So they have some conversion from these distances to probabilities.

There 's  you have  you have the paper , right ? The Mississippi State paper ?


There 's  you have  you have the paper , right ? The Mississippi State paper ?


There 's  you have  you have the paper , right ? The Mississippi State paper ?

Yeah , if you 're interested y you could look ,


Yeah , if you 're interested y you could look ,


So in your  in  in the thing that you 're doing , uh you have a vector of ones and zeros for each phone ?


So in your  in  in the thing that you 're doing , uh you have a vector of ones and zeros for each phone ?

Mm - hmm. Uh , is this the class project , or  ?


Mm - hmm. Uh , is this the class project , or  ?


Right ,  Right , right f so for every phone there is  there is a um  a vector of ones and zeros  f uh corresponding to whether it exhibits a particular phonological feature or not.


Right ,  Right , right f so for every phone there is  there is a um  a vector of ones and zeros  f uh corresponding to whether it exhibits a particular phonological feature or not.

Mm - hmm. Mm - hmm. And so when you do your wh I 'm  what is the task for the class project ? To come up with the phones ?


Right ,  Right , right f so for every phone there is  there is a um  a vector of ones and zeros  f uh corresponding to whether it exhibits a particular phonological feature or not.

Mm - hmm. Mm - hmm. And so when you do your wh I 'm  what is the task for the class project ? To come up with the phones ?

or to come up with these vectors to see how closely they match the phones ,


Mm - hmm. Mm - hmm. And so when you do your wh I 'm  what is the task for the class project ? To come up with the phones ?

or to come up with these vectors to see how closely they match the phones ,

Oh. Right , um to come up with a mapping from um MFCC 's or s some feature set ,  um to  uh w to whether there 's existence of a particular phonological feature.


or to come up with these vectors to see how closely they match the phones ,

Oh. Right , um to come up with a mapping from um MFCC 's or s some feature set ,  um to  uh w to whether there 's existence of a particular phonological feature.


or to come up with these vectors to see how closely they match the phones ,

Oh. Right , um to come up with a mapping from um MFCC 's or s some feature set ,  um to  uh w to whether there 's existence of a particular phonological feature.

And um yeah , basically it 's to learn a mapping  from   from the MFCC 's to  uh phonological features. Is it  did that answer your question ?


Oh. Right , um to come up with a mapping from um MFCC 's or s some feature set ,  um to  uh w to whether there 's existence of a particular phonological feature.

And um yeah , basically it 's to learn a mapping  from   from the MFCC 's to  uh phonological features. Is it  did that answer your question ?


And um yeah , basically it 's to learn a mapping  from   from the MFCC 's to  uh phonological features. Is it  did that answer your question ?


And um yeah , basically it 's to learn a mapping  from   from the MFCC 's to  uh phonological features. Is it  did that answer your question ?

I guess  I mean , uh  I 'm not sure what you  what you 're  what you get out of your system. Do you get out a uh  a vector of these ones and zeros and then try to find the closest matching phoneme to that vector ,


I guess  I mean , uh  I 'm not sure what you  what you 're  what you get out of your system. Do you get out a uh  a vector of these ones and zeros and then try to find the closest matching phoneme to that vector ,


I guess  I mean , uh  I 'm not sure what you  what you 're  what you get out of your system. Do you get out a uh  a vector of these ones and zeros and then try to find the closest matching phoneme to that vector ,

No , no. I 'm not  I 'm not planning to do any  any phoneme mapping yet. Just   it 's  it 's basically  it 's  it 's really simple , basically a detection  of phonological features.


No , no. I 'm not  I 'm not planning to do any  any phoneme mapping yet. Just   it 's  it 's basically  it 's  it 's really simple , basically a detection  of phonological features.


and um   cuz the uh  So King and  and Taylor  um did this with uh recurrent neural nets ,


and um   cuz the uh  So King and  and Taylor  um did this with uh recurrent neural nets ,

and this i their  their idea was to first find  a mapping from MFCC 's to  uh phonological features


and this i their  their idea was to first find  a mapping from MFCC 's to  uh phonological features

and then later on , once you have these  phonological features ,  then uh map that to phones.


and then later on , once you have these  phonological features ,  then uh map that to phones.

So I 'm  I 'm sort of reproducing phase one of their stuff.


and then later on , once you have these  phonological features ,  then uh map that to phones.

So I 'm  I 'm sort of reproducing phase one of their stuff.

Mmm. So they had one recurrent net for each particular feature ?


So I 'm  I 'm sort of reproducing phase one of their stuff.

Mmm. So they had one recurrent net for each particular feature ?


So I 'm  I 'm sort of reproducing phase one of their stuff.

Mmm. So they had one recurrent net for each particular feature ?

I see. I wo did they compare that  I mean , what if you just did phone recognition and did the reverse lookup.


Mmm. So they had one recurrent net for each particular feature ?

I see. I wo did they compare that  I mean , what if you just did phone recognition and did the reverse lookup.


I see. I wo did they compare that  I mean , what if you just did phone recognition and did the reverse lookup.

So you recognize a phone and which ever phone was recognized , you spit out it 's vector of ones and zeros.


So you recognize a phone and which ever phone was recognized , you spit out it 's vector of ones and zeros.


That 's probably not what he 's going to do on his class project. Yeah.


That 's probably not what he 's going to do on his class project. Yeah.

So um have you had a chance to do this um thing we talked about yet with the uh  um


So um have you had a chance to do this um thing we talked about yet with the uh  um


So um have you had a chance to do this um thing we talked about yet with the uh  um

Uh. No actually I was going a different  That 's a good question , too , but I was gonna ask about the   the um  changes to the data in comparing PLP and mel cepstrum for the SRI system.


So um have you had a chance to do this um thing we talked about yet with the uh  um

Uh. No actually I was going a different  That 's a good question , too , but I was gonna ask about the   the um  changes to the data in comparing PLP and mel cepstrum for the SRI system.

Uh. Well what I 've been  " Changes to the data " , I 'm not sure I


Uh. No actually I was going a different  That 's a good question , too , but I was gonna ask about the   the um  changes to the data in comparing PLP and mel cepstrum for the SRI system.

Uh. Well what I 've been  " Changes to the data " , I 'm not sure I

Right. So we talked on the phone about this , that  that there was still a difference of a  of a few percent


Uh. Well what I 've been  " Changes to the data " , I 'm not sure I

Right. So we talked on the phone about this , that  that there was still a difference of a  of a few percent

and  you told me that there was a difference in how the normalization was done. And I was asking if you were going to do   redo it uh for PLP with the normalization done as it had been done for the mel cepstrum.


Right. So we talked on the phone about this , that  that there was still a difference of a  of a few percent

and  you told me that there was a difference in how the normalization was done. And I was asking if you were going to do   redo it uh for PLP with the normalization done as it had been done for the mel cepstrum.

Mm - hmm. Uh right , no I haven't had a chance to do that.


and  you told me that there was a difference in how the normalization was done. And I was asking if you were going to do   redo it uh for PLP with the normalization done as it had been done for the mel cepstrum.

Mm - hmm. Uh right , no I haven't had a chance to do that.


and  you told me that there was a difference in how the normalization was done. And I was asking if you were going to do   redo it uh for PLP with the normalization done as it had been done for the mel cepstrum.

Mm - hmm. Uh right , no I haven't had a chance to do that.

What I 've been doing is  uh  trying to figure out  it just seems to me like there 's a um  well it seems like there 's a bug , because the difference in performance is  it 's not gigantic but it 's big enough that it  it seems wrong.


Mm - hmm. Uh right , no I haven't had a chance to do that.

What I 've been doing is  uh  trying to figure out  it just seems to me like there 's a um  well it seems like there 's a bug , because the difference in performance is  it 's not gigantic but it 's big enough that it  it seems wrong.

Yeah , I agree , but I thought that the normalization difference was one of the possibilities ,


What I 've been doing is  uh  trying to figure out  it just seems to me like there 's a um  well it seems like there 's a bug , because the difference in performance is  it 's not gigantic but it 's big enough that it  it seems wrong.

Yeah , I agree , but I thought that the normalization difference was one of the possibilities ,


Yeah , I agree , but I thought that the normalization difference was one of the possibilities ,

Yeah , I guess I don't think that the normalization difference is gonna account for everything.


Yeah , I guess I don't think that the normalization difference is gonna account for everything.


Yeah , I guess I don't think that the normalization difference is gonna account for everything.

So what I was working on is um just going through and checking the headers of the wavefiles , to see if maybe there was a um  a certain type of compression or something that was done that my script wasn't catching. So that for some subset of the training data , uh the  the  the features I was computing were junk.


So what I was working on is um just going through and checking the headers of the wavefiles , to see if maybe there was a um  a certain type of compression or something that was done that my script wasn't catching. So that for some subset of the training data , uh the  the  the features I was computing were junk.

Which would you know cause it to perform OK , but uh , you know , the  the models would be all messed up. So I was going through and just double - checking that kind of think first , to see if there was just some kind of obvious bug in the way that I was computing the features.


Which would you know cause it to perform OK , but uh , you know , the  the models would be all messed up. So I was going through and just double - checking that kind of think first , to see if there was just some kind of obvious bug in the way that I was computing the features.

Looking at all the sampling rates to make sure all the sampling rates were what  eight K , what I was assuming they were ,


Looking at all the sampling rates to make sure all the sampling rates were what  eight K , what I was assuming they were ,


Looking at all the sampling rates to make sure all the sampling rates were what  eight K , what I was assuming they were ,

Yeah , that makes sense , to check all that.


Yeah , that makes sense , to check all that.

Yeah. So I was doing that first , before I did these other things , just to make sure there wasn't something


Yeah , that makes sense , to check all that.

Yeah. So I was doing that first , before I did these other things , just to make sure there wasn't something

Although really , uh uh , a couple three percent uh difference in word error rate uh  could easily come from some difference in normalization , I would think. But


Yeah , that makes sense , to check all that.

Yeah. So I was doing that first , before I did these other things , just to make sure there wasn't something

Although really , uh uh , a couple three percent uh difference in word error rate uh  could easily come from some difference in normalization , I would think. But

Yeah , and I think , hhh   I 'm trying to remember but I think I recall that Andreas was saying that he was gonna run sort of the reverse experiment. Uh which is to try to emulate the normalization that we did but with the mel cepstral features. Sort of , you know , back up from the system that he had. I thought he said he was gonna  I have to look back through my  my email from him.


Yeah. So I was doing that first , before I did these other things , just to make sure there wasn't something

Although really , uh uh , a couple three percent uh difference in word error rate uh  could easily come from some difference in normalization , I would think. But

Yeah , and I think , hhh   I 'm trying to remember but I think I recall that Andreas was saying that he was gonna run sort of the reverse experiment. Uh which is to try to emulate the normalization that we did but with the mel cepstral features. Sort of , you know , back up from the system that he had. I thought he said he was gonna  I have to look back through my  my email from him.

Yeah , he 's probably off at  at uh his meeting now ,


Although really , uh uh , a couple three percent uh difference in word error rate uh  could easily come from some difference in normalization , I would think. But

Yeah , and I think , hhh   I 'm trying to remember but I think I recall that Andreas was saying that he was gonna run sort of the reverse experiment. Uh which is to try to emulate the normalization that we did but with the mel cepstral features. Sort of , you know , back up from the system that he had. I thought he said he was gonna  I have to look back through my  my email from him.

Yeah , he 's probably off at  at uh his meeting now ,


Yeah , and I think , hhh   I 'm trying to remember but I think I recall that Andreas was saying that he was gonna run sort of the reverse experiment. Uh which is to try to emulate the normalization that we did but with the mel cepstral features. Sort of , you know , back up from the system that he had. I thought he said he was gonna  I have to look back through my  my email from him.

Yeah , he 's probably off at  at uh his meeting now ,


Yeah , he 's probably off at  at uh his meeting now ,


the  I sh think they should be  roughly equivalent , um I mean again the Cambridge folk found the PLP actually to be a little better. Uh So it 's   um


the  I sh think they should be  roughly equivalent , um I mean again the Cambridge folk found the PLP actually to be a little better. Uh So it 's   um

I mean the other thing I wonder about was whether there was something just in the  the bootstrapping of their system which was based on  but maybe not , since they


the  I sh think they should be  roughly equivalent , um I mean again the Cambridge folk found the PLP actually to be a little better. Uh So it 's   um

I mean the other thing I wonder about was whether there was something just in the  the bootstrapping of their system which was based on  but maybe not , since they

Yeah see one thing that 's a little bit um  I was looking  I 've been studying and going through the logs for the system that um Andreas created. And um his uh  the way that the    S R I system looks like it works is that it reads the wavefiles directly , uh and does all of the cepstral computation stuff on the fly.


I mean the other thing I wonder about was whether there was something just in the  the bootstrapping of their system which was based on  but maybe not , since they

Yeah see one thing that 's a little bit um  I was looking  I 've been studying and going through the logs for the system that um Andreas created. And um his uh  the way that the    S R I system looks like it works is that it reads the wavefiles directly , uh and does all of the cepstral computation stuff on the fly.


I mean the other thing I wonder about was whether there was something just in the  the bootstrapping of their system which was based on  but maybe not , since they

Yeah see one thing that 's a little bit um  I was looking  I 've been studying and going through the logs for the system that um Andreas created. And um his uh  the way that the    S R I system looks like it works is that it reads the wavefiles directly , uh and does all of the cepstral computation stuff on the fly.

And , so there 's no place where these  where the cepstral files are stored , anywhere that I can go look at and compare to the PLP ones , so whereas with our features , he 's actually storing the cepstrum on disk , and he reads those in.


Yeah see one thing that 's a little bit um  I was looking  I 've been studying and going through the logs for the system that um Andreas created. And um his uh  the way that the    S R I system looks like it works is that it reads the wavefiles directly , uh and does all of the cepstral computation stuff on the fly.

And , so there 's no place where these  where the cepstral files are stored , anywhere that I can go look at and compare to the PLP ones , so whereas with our features , he 's actually storing the cepstrum on disk , and he reads those in.


And , so there 's no place where these  where the cepstral files are stored , anywhere that I can go look at and compare to the PLP ones , so whereas with our features , he 's actually storing the cepstrum on disk , and he reads those in.

But it looked like he had to give it  uh even though the cepstrum is already computed , he has to give it uh a front - end parameter file. Which talks about the kind of uh com computation that his mel cepstrum thing does ,


But it looked like he had to give it  uh even though the cepstrum is already computed , he has to give it uh a front - end parameter file. Which talks about the kind of uh com computation that his mel cepstrum thing does ,

so i I  I don't know if that  it probably doesn't mess it up , it probably just ignores it if it determines that it 's already in the right format or something but  the  the  the two processes that happen are a little different.


so i I  I don't know if that  it probably doesn't mess it up , it probably just ignores it if it determines that it 's already in the right format or something but  the  the  the two processes that happen are a little different.


so i I  I don't know if that  it probably doesn't mess it up , it probably just ignores it if it determines that it 's already in the right format or something but  the  the  the two processes that happen are a little different.

So anyway , there 's stuff there to sort out.


So anyway , there 's stuff there to sort out.


So anyway , there 's stuff there to sort out.

So , OK. Let 's go back to what you thought I was asking you.


So anyway , there 's stuff there to sort out.

So , OK. Let 's go back to what you thought I was asking you.

Yeah no and I didn't have a chance to do that.


So , OK. Let 's go back to what you thought I was asking you.

Yeah no and I didn't have a chance to do that.

Ha ! Oh ! You had the sa same answer anyway.


So , OK. Let 's go back to what you thought I was asking you.

Yeah no and I didn't have a chance to do that.

Ha ! Oh ! You had the sa same answer anyway.

Yeah. Yeah. I 've been um ,  I 've been working with um Jeremy on his project and then I 've been trying to track down this bug in uh the ICSI front - end features.


Yeah no and I didn't have a chance to do that.

Ha ! Oh ! You had the sa same answer anyway.

Yeah. Yeah. I 've been um ,  I 've been working with um Jeremy on his project and then I 've been trying to track down this bug in uh the ICSI front - end features.


Ha ! Oh ! You had the sa same answer anyway.

Yeah. Yeah. I 've been um ,  I 've been working with um Jeremy on his project and then I 've been trying to track down this bug in uh the ICSI front - end features.

So one thing that I did notice , yesterday I was studying the um  the uh RASTA code


Yeah. Yeah. I 've been um ,  I 've been working with um Jeremy on his project and then I 've been trying to track down this bug in uh the ICSI front - end features.

So one thing that I did notice , yesterday I was studying the um  the uh RASTA code


So one thing that I did notice , yesterday I was studying the um  the uh RASTA code

and it looks like we don't have any way to um control the frequency range that we use in our analysis. We basically  it looks to me like we do the FFT , um and then we just take all the bins and we use everything. We don't have any set of parameters where we can say you know , " only process from you know a hundred and ten hertz to thirty - seven - fifty ".


and it looks like we don't have any way to um control the frequency range that we use in our analysis. We basically  it looks to me like we do the FFT , um and then we just take all the bins and we use everything. We don't have any set of parameters where we can say you know , " only process from you know a hundred and ten hertz to thirty - seven - fifty ".

At least I couldn't see any kind of control for that.


and it looks like we don't have any way to um control the frequency range that we use in our analysis. We basically  it looks to me like we do the FFT , um and then we just take all the bins and we use everything. We don't have any set of parameters where we can say you know , " only process from you know a hundred and ten hertz to thirty - seven - fifty ".

At least I couldn't see any kind of control for that.

Yeah , I don't think it 's in there , I think it 's in the uh uh uh the filters. So , the F F T is on everything , but the filters um , for instance , ignore the  the lowest bins and the highest bins. And what it does is it  it copies


At least I couldn't see any kind of control for that.

Yeah , I don't think it 's in there , I think it 's in the uh uh uh the filters. So , the F F T is on everything , but the filters um , for instance , ignore the  the lowest bins and the highest bins. And what it does is it  it copies


At least I couldn't see any kind of control for that.

Yeah , I don't think it 's in there , I think it 's in the uh uh uh the filters. So , the F F T is on everything , but the filters um , for instance , ignore the  the lowest bins and the highest bins. And what it does is it  it copies

um The filter bank which is created by integrating over F F T bins.


Yeah , I don't think it 's in there , I think it 's in the uh uh uh the filters. So , the F F T is on everything , but the filters um , for instance , ignore the  the lowest bins and the highest bins. And what it does is it  it copies

um The filter bank which is created by integrating over F F T bins.


um The filter bank which is created by integrating over F F T bins.


um The filter bank which is created by integrating over F F T bins.

When you get the mel  When you go to the mel scale.


When you get the mel  When you go to the mel scale.

Right. Yeah , it 's bark scale , and it 's  it  it um  it actually copies the uh um  the second filters over to the first. So the first filters are always  and you can s you can specify a different number of  uh features  different number of filters , I think , as I recall. So you can specify a different number of filters , and whatever  um uh you specify , the last ones are gonna be ignored. So that  that 's a way that you sort of change what the  what the bandwidth is. Y you can't do it without I think changing the number of filters , but


When you get the mel  When you go to the mel scale.

Right. Yeah , it 's bark scale , and it 's  it  it um  it actually copies the uh um  the second filters over to the first. So the first filters are always  and you can s you can specify a different number of  uh features  different number of filters , I think , as I recall. So you can specify a different number of filters , and whatever  um uh you specify , the last ones are gonna be ignored. So that  that 's a way that you sort of change what the  what the bandwidth is. Y you can't do it without I think changing the number of filters , but

I saw something about uh  that looked like it was doing something like that , but I didn't quite understand it. So maybe


When you get the mel  When you go to the mel scale.

Right. Yeah , it 's bark scale , and it 's  it  it um  it actually copies the uh um  the second filters over to the first. So the first filters are always  and you can s you can specify a different number of  uh features  different number of filters , I think , as I recall. So you can specify a different number of filters , and whatever  um uh you specify , the last ones are gonna be ignored. So that  that 's a way that you sort of change what the  what the bandwidth is. Y you can't do it without I think changing the number of filters , but

I saw something about uh  that looked like it was doing something like that , but I didn't quite understand it. So maybe

Yeah , so the idea is that the very lowest frequencies and  and typically the veriest  highest frequencies are kind of junk.


Right. Yeah , it 's bark scale , and it 's  it  it um  it actually copies the uh um  the second filters over to the first. So the first filters are always  and you can s you can specify a different number of  uh features  different number of filters , I think , as I recall. So you can specify a different number of filters , and whatever  um uh you specify , the last ones are gonna be ignored. So that  that 's a way that you sort of change what the  what the bandwidth is. Y you can't do it without I think changing the number of filters , but

I saw something about uh  that looked like it was doing something like that , but I didn't quite understand it. So maybe

Yeah , so the idea is that the very lowest frequencies and  and typically the veriest  highest frequencies are kind of junk.


I saw something about uh  that looked like it was doing something like that , but I didn't quite understand it. So maybe

Yeah , so the idea is that the very lowest frequencies and  and typically the veriest  highest frequencies are kind of junk.

And so um you just  for continuity you just approximate them by   by the second to highest and second to lowest. It 's just a simple thing we put in.


Yeah , so the idea is that the very lowest frequencies and  and typically the veriest  highest frequencies are kind of junk.

And so um you just  for continuity you just approximate them by   by the second to highest and second to lowest. It 's just a simple thing we put in.


And so um you just  for continuity you just approximate them by   by the second to highest and second to lowest. It 's just a simple thing we put in.


And so um you just  for continuity you just approximate them by   by the second to highest and second to lowest. It 's just a simple thing we put in.

But  so the  but that 's a fixed uh thing ?


But  so the  but that 's a fixed uh thing ?


But  so the  but that 's a fixed uh thing ?

But see  see my point ? If you had   If you had ten filters ,  then you would be throwing away a lot at the two ends.


But see  see my point ? If you had   If you had ten filters ,  then you would be throwing away a lot at the two ends.


But see  see my point ? If you had   If you had ten filters ,  then you would be throwing away a lot at the two ends.

And if you had  if you had fifty filters , you 'd be throwing away hardly anything.


And if you had  if you had fifty filters , you 'd be throwing away hardly anything.

Um , I don't remember there being an independent way of saying " we 're just gonna make them from here to here ".


And if you had  if you had fifty filters , you 'd be throwing away hardly anything.

Um , I don't remember there being an independent way of saying " we 're just gonna make them from here to here ".

Use this analysis bandwidth or something.


Um , I don't remember there being an independent way of saying " we 're just gonna make them from here to here ".

Use this analysis bandwidth or something.

But I  I  I don't know , it 's actually been awhile since I 've looked at it.


Um , I don't remember there being an independent way of saying " we 're just gonna make them from here to here ".

Use this analysis bandwidth or something.

But I  I  I don't know , it 's actually been awhile since I 've looked at it.

Yeah , I went through the Feacalc code and then looked at you know just calling the RASTA libs  and thing like that. And I didn't  I couldn't see any wh place where that kind of thing was done. But um I didn't quite understand everything that I saw ,


Use this analysis bandwidth or something.

But I  I  I don't know , it 's actually been awhile since I 've looked at it.

Yeah , I went through the Feacalc code and then looked at you know just calling the RASTA libs  and thing like that. And I didn't  I couldn't see any wh place where that kind of thing was done. But um I didn't quite understand everything that I saw ,


But I  I  I don't know , it 's actually been awhile since I 've looked at it.

Yeah , I went through the Feacalc code and then looked at you know just calling the RASTA libs  and thing like that. And I didn't  I couldn't see any wh place where that kind of thing was done. But um I didn't quite understand everything that I saw ,


Yeah , I went through the Feacalc code and then looked at you know just calling the RASTA libs  and thing like that. And I didn't  I couldn't see any wh place where that kind of thing was done. But um I didn't quite understand everything that I saw ,

But it calls RASTA with some options , and um


But it calls RASTA with some options , and um


But it calls RASTA with some options , and um

But I  I think in  I don't know. I guess for some particular database you might find that you could tune that and tweak that to get that a little better , but I think that  in general it 's not that critical. I mean there 's


But I  I think in  I don't know. I guess for some particular database you might find that you could tune that and tweak that to get that a little better , but I think that  in general it 's not that critical. I mean there 's

You can  You can throw away stuff below a hundred hertz or so and it 's just not going to affect phonetic classification at all.


But I  I think in  I don't know. I guess for some particular database you might find that you could tune that and tweak that to get that a little better , but I think that  in general it 's not that critical. I mean there 's

You can  You can throw away stuff below a hundred hertz or so and it 's just not going to affect phonetic classification at all.

Another thing I was thinking about was um is there a  I was wondering if there 's maybe um  certain settings of the parameters when you compute PLP which would basically cause it to output mel cepstrum. So that , in effect , what I could do is use our code but produce mel cepstrum and compare that directly to


You can  You can throw away stuff below a hundred hertz or so and it 's just not going to affect phonetic classification at all.

Another thing I was thinking about was um is there a  I was wondering if there 's maybe um  certain settings of the parameters when you compute PLP which would basically cause it to output mel cepstrum. So that , in effect , what I could do is use our code but produce mel cepstrum and compare that directly to

Well , it 's not precisely. Yeah. I mean ,


Another thing I was thinking about was um is there a  I was wondering if there 's maybe um  certain settings of the parameters when you compute PLP which would basically cause it to output mel cepstrum. So that , in effect , what I could do is use our code but produce mel cepstrum and compare that directly to

Well , it 's not precisely. Yeah. I mean ,

um ,  um what you can do is um you can definitely change the  the filter bank from being uh a uh trapezoidal integration to a  a  a triangular one ,


Well , it 's not precisely. Yeah. I mean ,

um ,  um what you can do is um you can definitely change the  the filter bank from being uh a uh trapezoidal integration to a  a  a triangular one ,


um ,  um what you can do is um you can definitely change the  the filter bank from being uh a uh trapezoidal integration to a  a  a triangular one ,

which is what the typical mel  mel cepstral uh filter bank does.


which is what the typical mel  mel cepstral uh filter bank does.

And some people have claimed that they got some better performance doing that , so you certainly could do that easily. But the fundamental difference , I mean , there 's other small differences


which is what the typical mel  mel cepstral uh filter bank does.

And some people have claimed that they got some better performance doing that , so you certainly could do that easily. But the fundamental difference , I mean , there 's other small differences

There 's a cubic root that happens , right ?


And some people have claimed that they got some better performance doing that , so you certainly could do that easily. But the fundamental difference , I mean , there 's other small differences

There 's a cubic root that happens , right ?

Yeah , but , you know , as opposed to the log in the other case. I mean  the fundamental d d difference that we 've seen any kind of difference from before , which is actually an advantage for the P L P i uh , I think , is that the  the smoothing at the end is auto - regressive instead of being cepstral  uh ,  from cepstral truncation. So um it 's a little more noise robust.


There 's a cubic root that happens , right ?

Yeah , but , you know , as opposed to the log in the other case. I mean  the fundamental d d difference that we 've seen any kind of difference from before , which is actually an advantage for the P L P i uh , I think , is that the  the smoothing at the end is auto - regressive instead of being cepstral  uh ,  from cepstral truncation. So um it 's a little more noise robust.

Um , and that 's  that 's why when people started getting databases that had a little more noise in it , like  like uh um Broadcast News and so on , that 's why c Cambridge switched to PLP I think.


Yeah , but , you know , as opposed to the log in the other case. I mean  the fundamental d d difference that we 've seen any kind of difference from before , which is actually an advantage for the P L P i uh , I think , is that the  the smoothing at the end is auto - regressive instead of being cepstral  uh ,  from cepstral truncation. So um it 's a little more noise robust.

Um , and that 's  that 's why when people started getting databases that had a little more noise in it , like  like uh um Broadcast News and so on , that 's why c Cambridge switched to PLP I think.


Um , and that 's  that 's why when people started getting databases that had a little more noise in it , like  like uh um Broadcast News and so on , that 's why c Cambridge switched to PLP I think.

So um That 's a difference that I don't  think we put any way to get around , since it was an advantage. um  uh


So um That 's a difference that I don't  think we put any way to get around , since it was an advantage. um  uh

but we did  eh we did hear this comment from people at some point , that  um it uh they got some better results with the triangular filters rather than the trapezoidal. So that is an option in RASTA.


but we did  eh we did hear this comment from people at some point , that  um it uh they got some better results with the triangular filters rather than the trapezoidal. So that is an option in RASTA.

Uh and you can certainly play with that. But I think you 're probably doing the right thing to look for bugs first. I don't know.


but we did  eh we did hear this comment from people at some point , that  um it uh they got some better results with the triangular filters rather than the trapezoidal. So that is an option in RASTA.

Uh and you can certainly play with that. But I think you 're probably doing the right thing to look for bugs first. I don't know.

Yeah just  it just seems like this kind of behavior could be caused by you know s some of the training data being messed up.


Uh and you can certainly play with that. But I think you 're probably doing the right thing to look for bugs first. I don't know.

Yeah just  it just seems like this kind of behavior could be caused by you know s some of the training data being messed up.


Uh and you can certainly play with that. But I think you 're probably doing the right thing to look for bugs first. I don't know.

Yeah just  it just seems like this kind of behavior could be caused by you know s some of the training data being messed up.

You know , you 're sort of getting most of the way there , but there 's a  So I started going through and looking  One of the things that I did notice was that the um log likelihoods coming out of the log recognizer from the PLP data were much lower , much smaller , than for the mel cepstral stuff , and that the average amount of pruning that was happening was therefore a little bit higher for the PLP features.


Yeah just  it just seems like this kind of behavior could be caused by you know s some of the training data being messed up.

You know , you 're sort of getting most of the way there , but there 's a  So I started going through and looking  One of the things that I did notice was that the um log likelihoods coming out of the log recognizer from the PLP data were much lower , much smaller , than for the mel cepstral stuff , and that the average amount of pruning that was happening was therefore a little bit higher for the PLP features.


You know , you 're sort of getting most of the way there , but there 's a  So I started going through and looking  One of the things that I did notice was that the um log likelihoods coming out of the log recognizer from the PLP data were much lower , much smaller , than for the mel cepstral stuff , and that the average amount of pruning that was happening was therefore a little bit higher for the PLP features.

So , since he used the same exact pruning thresholds for both , I was wondering if it could be that we 're getting more pruning.


You know , you 're sort of getting most of the way there , but there 's a  So I started going through and looking  One of the things that I did notice was that the um log likelihoods coming out of the log recognizer from the PLP data were much lower , much smaller , than for the mel cepstral stuff , and that the average amount of pruning that was happening was therefore a little bit higher for the PLP features.

So , since he used the same exact pruning thresholds for both , I was wondering if it could be that we 're getting more pruning.

Oh ! He  he   He used the identical pruning thresholds even though the s the range of p of the likeli


So , since he used the same exact pruning thresholds for both , I was wondering if it could be that we 're getting more pruning.

Oh ! He  he   He used the identical pruning thresholds even though the s the range of p of the likeli


So , since he used the same exact pruning thresholds for both , I was wondering if it could be that we 're getting more pruning.

Oh ! He  he   He used the identical pruning thresholds even though the s the range of p of the likeli

Oh well that 's   That 's a pretty good  point right there.


Oh ! He  he   He used the identical pruning thresholds even though the s the range of p of the likeli

Oh well that 's   That 's a pretty good  point right there.