Okay odd shape with spongy  cover. And standard battery okay , a chip imprint , there's no specific problem. So we agree to put the wheel

Mm-hmm.  Wheel on the top. Button , where do we want some buttons ?

Well , usually hold   'Kay , we want it to be good also for the left-hand users , right ?


Mm-hmm.  Wheel on the top. Button , where do we want some buttons ?

Well , usually hold   'Kay , we want it to be good also for the left-hand users , right ?

So it have to  it has to be symmetrical.


Well , usually hold   'Kay , we want it to be good also for the left-hand users , right ?

So it have to  it has to be symmetrical.

Uh   Yeah but okay. Sa let's say that th o  It has to be  basically you can only take two sides , one on ths this side where is the thumb and the other side where there is uh yeah also the thumb.


So it have to  it has to be symmetrical.

Uh   Yeah but okay. Sa let's say that th o  It has to be  basically you can only take two sides , one on ths this side where is the thumb and the other side where there is uh yeah also the thumb.


Uh   Yeah but okay. Sa let's say that th o  It has to be  basically you can only take two sides , one on ths this side where is the thumb and the other side where there is uh yeah also the thumb.

Basically. Or you could use use this one , but I don't know if it's very comfortable , to use this one for the wheel.


Basically. Or you could use use this one , but I don't know if it's very comfortable , to use this one for the wheel.

Yeah maybe the thumb is more comfortable.


Basically. Or you could use use this one , but I don't know if it's very comfortable , to use this one for the wheel.

Yeah maybe the thumb is more comfortable.

This for the wheel and then this for the buttons ?


Basically. Or you could use use this one , but I don't know if it's very comfortable , to use this one for the wheel.

Yeah maybe the thumb is more comfortable.

This for the wheel and then this for the buttons ?

Yeah I think it's okay for both right and left. Mm.


Yeah maybe the thumb is more comfortable.

This for the wheel and then this for the buttons ?

Yeah I think it's okay for both right and left. Mm.


This for the wheel and then this for the buttons ?

Yeah I think it's okay for both right and left. Mm.


Yeah I think it's okay for both right and left. Mm.


I think you can turn it this way also. You can do both with both hands.


I think you can turn it this way also. You can do both with both hands.

Yeah , the problem is if you have buttons and wheel then when you turn it around , the buttons are on the other side. So you cannot see them.


Yeah , the problem is if you have buttons and wheel then when you turn it around , the buttons are on the other side. So you cannot see them.


Yeah , the problem is if you have buttons and wheel then when you turn it around , the buttons are on the other side. So you cannot see them.

And moreover , th the button ar are lighted so you you immediately identify the right side , because you have light on buttons.


And moreover , th the button ar are lighted so you you immediately identify the right side , because you have light on buttons.

So the buttons have to be here and the wheel has to be  Y Yeah I know , but uh if you hold with your left hand , and the wheel is here , and the buttons are here , then when you turn it around the buttons will be on the other side.


And moreover , th the button ar are lighted so you you immediately identify the right side , because you have light on buttons.

So the buttons have to be here and the wheel has to be  Y Yeah I know , but uh if you hold with your left hand , and the wheel is here , and the buttons are here , then when you turn it around the buttons will be on the other side.

Yeah. No you you  I think you will use it only on the right or left hand , whether you are righty or lefty. I think for lefty it's okay. I can do this movement , and for righty as well. I think this doesn't change that much.


So the buttons have to be here and the wheel has to be  Y Yeah I know , but uh if you hold with your left hand , and the wheel is here , and the buttons are here , then when you turn it around the buttons will be on the other side.

Yeah. No you you  I think you will use it only on the right or left hand , whether you are righty or lefty. I think for lefty it's okay. I can do this movement , and for righty as well. I think this doesn't change that much.


Yeah. No you you  I think you will use it only on the right or left hand , whether you are righty or lefty. I think for lefty it's okay. I can do this movement , and for righty as well. I think this doesn't change that much.


So , for interface we said also that we have uh this banana shape with button on the s on the side. And  and uh a wheel on the top.


So , for interface we said also that we have uh this banana shape with button on the s on the side. And  and uh a wheel on the top.

So the colour is yellow. I think it's uh  we defined everything according to what we should  what the decision we should take , yeah. So maybe we can um we can uh work on those aspects uh until next meeting. So have the final uh look and feel design according to the decision. And have the the user interface design and uh then evaluate the prodyuc  the product. That is to say , uh check if it fit the the requirement uh given by the users , but according to uh your presentation it seems to be okay. It seems to be fancy , innovative , and easy to use.


So the colour is yellow. I think it's uh  we defined everything according to what we should  what the decision we should take , yeah. So maybe we can um we can uh work on those aspects uh until next meeting. So have the final uh look and feel design according to the decision. And have the the user interface design and uh then evaluate the prodyuc  the product. That is to say , uh check if it fit the the requirement uh given by the users , but according to uh your presentation it seems to be okay. It seems to be fancy , innovative , and easy to use.


Uh. Somebody else should run this. I 'm sick of being the one to sort of go through and say , " Well , what do you think about this ? " You wanna  ?


Uh. Somebody else should run this. I 'm sick of being the one to sort of go through and say , " Well , what do you think about this ? " You wanna  ?

Should we take turns ? You want me to run it today ?


Should we take turns ? You want me to run it today ?

OK. OK. Um. Let 's see , maybe we should just get a list of items  things that we should talk about. Um , I guess there 's the usual  updates , everybody going around and saying , uh , you know , what they 're working on , the things that happened the last week. But aside from that is there anything in particular that anybody wants to bring up


OK. OK. Um. Let 's see , maybe we should just get a list of items  things that we should talk about. Um , I guess there 's the usual  updates , everybody going around and saying , uh , you know , what they 're working on , the things that happened the last week. But aside from that is there anything in particular that anybody wants to bring up

for today ? No ? OK. So why don't we just around and people can give updates.


for today ? No ? OK. So why don't we just around and people can give updates.


for today ? No ? OK. So why don't we just around and people can give updates.

Alright. Um. Well , the first thing maybe is that the p Eurospeech paper is , uh , accepted. Um. Yeah.


Alright. Um. Well , the first thing maybe is that the p Eurospeech paper is , uh , accepted. Um. Yeah.

This is  what  what do you , uh  what 's in the paper there ?


Alright. Um. Well , the first thing maybe is that the p Eurospeech paper is , uh , accepted. Um. Yeah.

This is  what  what do you , uh  what 's in the paper there ?

So it 's the paper that describe basically the , um , system that were proposed for the  Aurora.


Alright. Um. Well , the first thing maybe is that the p Eurospeech paper is , uh , accepted. Um. Yeah.

This is  what  what do you , uh  what 's in the paper there ?

So it 's the paper that describe basically the , um , system that were proposed for the  Aurora.

The one that we s we submitted the last round ?


This is  what  what do you , uh  what 's in the paper there ?

So it 's the paper that describe basically the , um , system that were proposed for the  Aurora.

The one that we s we submitted the last round ?


So it 's the paper that describe basically the , um , system that were proposed for the  Aurora.

The one that we s we submitted the last round ?


The one that we s we submitted the last round ?


Um  Yeah. So and the , fff  comments seems  from the reviewer are good. So.


It 's , uh , Aalborg in Denmark. And it 's ,


Mmm  Yeah. Then , uh , whhh well , I 've been working on  on t mainly on on - line normalization this week. Uh , I 've been trying different  slightly  slightly different approaches. Um , the first thing is trying to play a little bit again with the , um , time constant. Uh , second thing is , uh , the training of , uh , on - line normalization with two different means , one mean for the silence and one for the speech. Um , and so I have two recursions which are controlled by the , um , probability of the voice activity detector. Mmm. This actually don't s doesn't seem to help , although it doesn't hurt. So. But  well , both  on - line normalization approach seems equivalent. Well , they


Mmm  Yeah. Then , uh , whhh well , I 've been working on  on t mainly on on - line normalization this week. Uh , I 've been trying different  slightly  slightly different approaches. Um , the first thing is trying to play a little bit again with the , um , time constant. Uh , second thing is , uh , the training of , uh , on - line normalization with two different means , one mean for the silence and one for the speech. Um , and so I have two recursions which are controlled by the , um , probability of the voice activity detector. Mmm. This actually don't s doesn't seem to help , although it doesn't hurt. So. But  well , both  on - line normalization approach seems equivalent. Well , they

Are the means pretty different  for the two ?


Mmm  Yeah. Then , uh , whhh well , I 've been working on  on t mainly on on - line normalization this week. Uh , I 've been trying different  slightly  slightly different approaches. Um , the first thing is trying to play a little bit again with the , um , time constant. Uh , second thing is , uh , the training of , uh , on - line normalization with two different means , one mean for the silence and one for the speech. Um , and so I have two recursions which are controlled by the , um , probability of the voice activity detector. Mmm. This actually don't s doesn't seem to help , although it doesn't hurt. So. But  well , both  on - line normalization approach seems equivalent. Well , they

Are the means pretty different  for the two ?

Yeah. They can be very different. Yeah. Mm - hmm.


Are the means pretty different  for the two ?

Yeah. They can be very different. Yeah. Mm - hmm.

So do you maybe make errors in different places ? Different kinds of errors ?


Yeah. They can be very different. Yeah. Mm - hmm.

So do you maybe make errors in different places ? Different kinds of errors ?

I didn't look , uh , more closely. Um. It might be , yeah. Mm - hmm. Um. Well , eh , there is one thing that we can observe , is that the mean are more different for  for C - zero and C - one than for the other coefficients. And  Yeah. And  Yeah , it  the C - one is  There are strange  strange thing happening with C - one , is that when you have different kind of noises , the mean for the  the silence portion is  can be different. And


So do you maybe make errors in different places ? Different kinds of errors ?

I didn't look , uh , more closely. Um. It might be , yeah. Mm - hmm. Um. Well , eh , there is one thing that we can observe , is that the mean are more different for  for C - zero and C - one than for the other coefficients. And  Yeah. And  Yeah , it  the C - one is  There are strange  strange thing happening with C - one , is that when you have different kind of noises , the mean for the  the silence portion is  can be different. And


So do you maybe make errors in different places ? Different kinds of errors ?

I didn't look , uh , more closely. Um. It might be , yeah. Mm - hmm. Um. Well , eh , there is one thing that we can observe , is that the mean are more different for  for C - zero and C - one than for the other coefficients. And  Yeah. And  Yeah , it  the C - one is  There are strange  strange thing happening with C - one , is that when you have different kind of noises , the mean for the  the silence portion is  can be different. And

So when you look at the trajectory of C - one , it 's  has a strange shape and I was expecting th the s that these two mean helps , especially because of the  the strange C - ze C - one shape , uh , which can  like , yo you can have , um , a trajectory for the speech and then when you are in the silence it goes somewhere , but if the noise is different it goes somewhere else.


I didn't look , uh , more closely. Um. It might be , yeah. Mm - hmm. Um. Well , eh , there is one thing that we can observe , is that the mean are more different for  for C - zero and C - one than for the other coefficients. And  Yeah. And  Yeah , it  the C - one is  There are strange  strange thing happening with C - one , is that when you have different kind of noises , the mean for the  the silence portion is  can be different. And

So when you look at the trajectory of C - one , it 's  has a strange shape and I was expecting th the s that these two mean helps , especially because of the  the strange C - ze C - one shape , uh , which can  like , yo you can have , um , a trajectory for the speech and then when you are in the silence it goes somewhere , but if the noise is different it goes somewhere else.


So when you look at the trajectory of C - one , it 's  has a strange shape and I was expecting th the s that these two mean helps , especially because of the  the strange C - ze C - one shape , uh , which can  like , yo you can have , um , a trajectory for the speech and then when you are in the silence it goes somewhere , but if the noise is different it goes somewhere else.

So which would mean that if we estimate the mean based on all the signal , even though we have frame dropping , but we don't frame ev uh , drop everything , but  uh , this can  hurts the estimation of the mean for speech , and  Mmm.  But I still have to investigate further , I think. Um , a third thing is , um ,  that instead of t having a fixed time constant , I try to have a time constant that 's smaller at the beginning of the utterances to adapt more quickly to the r something that 's closer to the right mean. T t um  Yeah. And then this time constant increases and I have a threshold that


So which would mean that if we estimate the mean based on all the signal , even though we have frame dropping , but we don't frame ev uh , drop everything , but  uh , this can  hurts the estimation of the mean for speech , and  Mmm.  But I still have to investigate further , I think. Um , a third thing is , um ,  that instead of t having a fixed time constant , I try to have a time constant that 's smaller at the beginning of the utterances to adapt more quickly to the r something that 's closer to the right mean. T t um  Yeah. And then this time constant increases and I have a threshold that

well , if it 's higher than a certain threshold , I keep it to this threshold to still , uh , adapt , um , the mean when   if the utterance is , uh , long enough to  to continue to adapt after , like , one second


well , if it 's higher than a certain threshold , I keep it to this threshold to still , uh , adapt , um , the mean when   if the utterance is , uh , long enough to  to continue to adapt after , like , one second

or  Mmm. Uh , well , this doesn't help neither , but this doesn't hurt. So , well. It seems pretty


well , if it 's higher than a certain threshold , I keep it to this threshold to still , uh , adapt , um , the mean when   if the utterance is , uh , long enough to  to continue to adapt after , like , one second

or  Mmm. Uh , well , this doesn't help neither , but this doesn't hurt. So , well. It seems pretty

Wasn't there some experiment you were gonna try where you did something differently for each , um ,  uh  I don't know whether it was each mel band or each , uh , um , FFT bin or someth There was something you were gonna  uh ,  some parameter you were gonna vary depending on the frequency. I don't know if that was


or  Mmm. Uh , well , this doesn't help neither , but this doesn't hurt. So , well. It seems pretty

Wasn't there some experiment you were gonna try where you did something differently for each , um ,  uh  I don't know whether it was each mel band or each , uh , um , FFT bin or someth There was something you were gonna  uh ,  some parameter you were gonna vary depending on the frequency. I don't know if that was

I guess it was  I don't know. No. u Maybe it 's this  this idea of having different  on - line normalization , um , tunings for the different MFCC 's.


Wasn't there some experiment you were gonna try where you did something differently for each , um ,  uh  I don't know whether it was each mel band or each , uh , um , FFT bin or someth There was something you were gonna  uh ,  some parameter you were gonna vary depending on the frequency. I don't know if that was

I guess it was  I don't know. No. u Maybe it 's this  this idea of having different  on - line normalization , um , tunings for the different MFCC 's.


I guess it was  I don't know. No. u Maybe it 's this  this idea of having different  on - line normalization , um , tunings for the different MFCC 's.


Yeah. I  I thought , Morgan , you brought it up a couple meetings ago. And then it was something about , uh , some and then somebody said " yeah , it does seem like , you know , C - zero is the one that 's , you know , the major one " or , uh , s I can't remember exactly what it was now.


Yeah. I  I thought , Morgan , you brought it up a couple meetings ago. And then it was something about , uh , some and then somebody said " yeah , it does seem like , you know , C - zero is the one that 's , you know , the major one " or , uh , s I can't remember exactly what it was now.

Mmm. Yeah. There  uh , actually , yeah. S um , it 's very important to normalize C - zero and  much less to normalize the other coefficients. And , um , actu uh , well , at least with the current on - line normalization scheme. And we  I think , we  kind of know that normalizing C - one doesn't help with the current scheme. And  and  Yeah. In my idea , I  I was thinking that the  the  the reason is maybe because of these funny things that happen between speech and silence which have different means. Um  Yeah. But maybe it 's not so   so easy to


Yeah. I  I thought , Morgan , you brought it up a couple meetings ago. And then it was something about , uh , some and then somebody said " yeah , it does seem like , you know , C - zero is the one that 's , you know , the major one " or , uh , s I can't remember exactly what it was now.

Mmm. Yeah. There  uh , actually , yeah. S um , it 's very important to normalize C - zero and  much less to normalize the other coefficients. And , um , actu uh , well , at least with the current on - line normalization scheme. And we  I think , we  kind of know that normalizing C - one doesn't help with the current scheme. And  and  Yeah. In my idea , I  I was thinking that the  the  the reason is maybe because of these funny things that happen between speech and silence which have different means. Um  Yeah. But maybe it 's not so   so easy to

Um , I I really would like to suggest looking , um , a little bit at the kinds of errors. I know you can get lost in that and go forever and not see too much , but   sometimes ,


Mmm. Yeah. There  uh , actually , yeah. S um , it 's very important to normalize C - zero and  much less to normalize the other coefficients. And , um , actu uh , well , at least with the current on - line normalization scheme. And we  I think , we  kind of know that normalizing C - one doesn't help with the current scheme. And  and  Yeah. In my idea , I  I was thinking that the  the  the reason is maybe because of these funny things that happen between speech and silence which have different means. Um  Yeah. But maybe it 's not so   so easy to

Um , I I really would like to suggest looking , um , a little bit at the kinds of errors. I know you can get lost in that and go forever and not see too much , but   sometimes ,

but  but , um , just seeing that each of these things didn't make things better may not be enough. It may be that they 're making them better in some ways and worse in others ,


Um , I I really would like to suggest looking , um , a little bit at the kinds of errors. I know you can get lost in that and go forever and not see too much , but   sometimes ,

but  but , um , just seeing that each of these things didn't make things better may not be enough. It may be that they 're making them better in some ways and worse in others ,


but  but , um , just seeing that each of these things didn't make things better may not be enough. It may be that they 're making them better in some ways and worse in others ,

or increasing insertions and decreasing deletions , or  or , um , um , you know , helping with noisy case but hurting in quiet case. And if you saw that then maybe you  it would   something would occur to you of how to deal with that.


or increasing insertions and decreasing deletions , or  or , um , um , you know , helping with noisy case but hurting in quiet case. And if you saw that then maybe you  it would   something would occur to you of how to deal with that.


or increasing insertions and decreasing deletions , or  or , um , um , you know , helping with noisy case but hurting in quiet case. And if you saw that then maybe you  it would   something would occur to you of how to deal with that.

Alright. Mmm. Yeah. W um , So that 's it , I think , for the on - line normalization. Um  Yeah. I 've been playing a little bit with some kind of thresholding , and , mmm , as a first experiment , I think I Yeah. Well , what I did is t is to take , um   to measure the average  no , the maximum energy of s each utterance and then put a threshold  Well , this for each mel band. Then put a threshold that 's fifteen DB below  well , uh , a couple of DB below this maximum ,


Alright. Mmm. Yeah. W um , So that 's it , I think , for the on - line normalization. Um  Yeah. I 've been playing a little bit with some kind of thresholding , and , mmm , as a first experiment , I think I Yeah. Well , what I did is t is to take , um   to measure the average  no , the maximum energy of s each utterance and then put a threshold  Well , this for each mel band. Then put a threshold that 's fifteen DB below  well , uh , a couple of DB below this maximum ,


Alright. Mmm. Yeah. W um , So that 's it , I think , for the on - line normalization. Um  Yeah. I 've been playing a little bit with some kind of thresholding , and , mmm , as a first experiment , I think I Yeah. Well , what I did is t is to take , um   to measure the average  no , the maximum energy of s each utterance and then put a threshold  Well , this for each mel band. Then put a threshold that 's fifteen DB below  well , uh , a couple of DB below this maximum ,

and  Actually it was not a threshold , it was just adding noise.


and  Actually it was not a threshold , it was just adding noise.

So I was adding a white noise energy , uh , that 's fifteen DB below the maximum energy of the utterance. And  Yeah. When we look at  at the , um , MFCC that result from this , they are  a lot more smoother. Um , when we compare , like , a channel zero and channel one utterance  um , so a clean and , uh , the same noisy utterance  well , there is almost no difference between the cepstral coefficients of the two.


So I was adding a white noise energy , uh , that 's fifteen DB below the maximum energy of the utterance. And  Yeah. When we look at  at the , um , MFCC that result from this , they are  a lot more smoother. Um , when we compare , like , a channel zero and channel one utterance  um , so a clean and , uh , the same noisy utterance  well , there is almost no difference between the cepstral coefficients of the two.

Um. And  Yeah. And the result that we have in term of speech recognition , actually it 's not  it 's not worse , it 's not better neither , but it 's , um , kind of surprising that it 's not worse


Um. And  Yeah. And the result that we have in term of speech recognition , actually it 's not  it 's not worse , it 's not better neither , but it 's , um , kind of surprising that it 's not worse

because basically you add noise that 's fifteen DB  just fifteen DB below  the maximum energy.


because basically you add noise that 's fifteen DB  just fifteen DB below  the maximum energy.


because basically you add noise that 's fifteen DB  just fifteen DB below  the maximum energy.

So why does that m  smooth things out ? I don't  I don't understand that.


So why does that m  smooth things out ? I don't  I don't understand that.

Well , there 's less difference. Right ?


So why does that m  smooth things out ? I don't  I don't understand that.

Well , there 's less difference. Right ?

It 's  I think , it 's whitening  This  the portion that are more silent ,


Well , there 's less difference. Right ?

It 's  I think , it 's whitening  This  the portion that are more silent ,

as you add a white noise that are  has a very high energy , it whitens everything


It 's  I think , it 's whitening  This  the portion that are more silent ,

as you add a white noise that are  has a very high energy , it whitens everything


as you add a white noise that are  has a very high energy , it whitens everything

and  and the high - energy portion of the speech don't get much affected anyway by the other noise. And as the noise you add is the same is   the shape , it 's also the same.


and  and the high - energy portion of the speech don't get much affected anyway by the other noise. And as the noise you add is the same is   the shape , it 's also the same.


and  and the high - energy portion of the speech don't get much affected anyway by the other noise. And as the noise you add is the same is   the shape , it 's also the same.

So they have  the trajectory are very , very similar. And  and


So they have  the trajectory are very , very similar. And  and

So , I mean , again , if you trained in one kind of noise and tested in the same kind of noise , you 'd  you know , given enough training data you don't do b do badly. The reason that we d that we have the problems we have is because  it 's different in training and test. Even if  the general kind is the same , the exact instances are different. And  and


So they have  the trajectory are very , very similar. And  and

So , I mean , again , if you trained in one kind of noise and tested in the same kind of noise , you 'd  you know , given enough training data you don't do b do badly. The reason that we d that we have the problems we have is because  it 's different in training and test. Even if  the general kind is the same , the exact instances are different. And  and

so when you whiten it , then it 's like you  the  the only noise  to  to first order , the only th noise that you have is white noise and you 've added the same thing to training and test.


So , I mean , again , if you trained in one kind of noise and tested in the same kind of noise , you 'd  you know , given enough training data you don't do b do badly. The reason that we d that we have the problems we have is because  it 's different in training and test. Even if  the general kind is the same , the exact instances are different. And  and

so when you whiten it , then it 's like you  the  the only noise  to  to first order , the only th noise that you have is white noise and you 've added the same thing to training and test.


so when you whiten it , then it 's like you  the  the only noise  to  to first order , the only th noise that you have is white noise and you 've added the same thing to training and test.


So would that  be similar to , like , doing the smoothing , then , over time or  ?


It 's  it 's something that  yeah , that affects more or less the silence portions because


It 's  it 's something that  yeah , that affects more or less the silence portions because

Well , anyway , the sp the portion of speech that ha have high energy are not ch a lot affected by the noises in the Aurora database.


Well , anyway , the sp the portion of speech that ha have high energy are not ch a lot affected by the noises in the Aurora database.

If  if you compare th the two shut channels of SpeechDat - Car during speech portion , it 's n n n the MFCC are not very different. They are very different when energy 's lower , like during fricatives or during speech pauses. And ,


Well , anyway , the sp the portion of speech that ha have high energy are not ch a lot affected by the noises in the Aurora database.

If  if you compare th the two shut channels of SpeechDat - Car during speech portion , it 's n n n the MFCC are not very different. They are very different when energy 's lower , like during fricatives or during speech pauses. And ,

Yeah , but you 're still getting more recognition errors ,


If  if you compare th the two shut channels of SpeechDat - Car during speech portion , it 's n n n the MFCC are not very different. They are very different when energy 's lower , like during fricatives or during speech pauses. And ,

Yeah , but you 're still getting more recognition errors ,


If  if you compare th the two shut channels of SpeechDat - Car during speech portion , it 's n n n the MFCC are not very different. They are very different when energy 's lower , like during fricatives or during speech pauses. And ,

Yeah , but you 're still getting more recognition errors ,

which means  that the differences , even though they look like they 're not so big ,  are  are hurting your recognition.


Yeah , but you 're still getting more recognition errors ,

which means  that the differences , even though they look like they 're not so big ,  are  are hurting your recognition.


which means  that the differences , even though they look like they 're not so big ,  are  are hurting your recognition.


Yeah. So , but in this case I  I really expect that maybe the  the two  these two stream of features , they are very different. I mean , and maybe we could gain something by combining them


Yeah. So , but in this case I  I really expect that maybe the  the two  these two stream of features , they are very different. I mean , and maybe we could gain something by combining them

Well , the other thing is that you just picked one particular way of doing it.


Yeah. So , but in this case I  I really expect that maybe the  the two  these two stream of features , they are very different. I mean , and maybe we could gain something by combining them

Well , the other thing is that you just picked one particular way of doing it.

Uh , I mean , first place it 's fifteen DB , uh ,  down across the utterance. And  maybe you 'd want to have something that was a little more adaptive. Secondly , you happened to pick fifteen DB


Well , the other thing is that you just picked one particular way of doing it.

Uh , I mean , first place it 's fifteen DB , uh ,  down across the utterance. And  maybe you 'd want to have something that was a little more adaptive. Secondly , you happened to pick fifteen DB


Uh , I mean , first place it 's fifteen DB , uh ,  down across the utterance. And  maybe you 'd want to have something that was a little more adaptive. Secondly , you happened to pick fifteen DB


So what was the  what was the threshold part of it ? Was the threshold , uh , how far down  ?


So what was the  what was the threshold part of it ? Was the threshold , uh , how far down  ?

Yeah. Well , he  yeah , he had to figure out how much to add. So he was looking  he was looking at the peak value.


Yeah. Well , he  yeah , he had to figure out how much to add. So he was looking  he was looking at the peak value.


And  and so what 's  ho I don't understand. How does it go ? If it  if  if the peak value 's above some threshold , then you add the noise ? Or if it 's below s


And  and so what 's  ho I don't understand. How does it go ? If it  if  if the peak value 's above some threshold , then you add the noise ? Or if it 's below s

I systematically  add the noise , but the , um , noise level is just  some kind of threshold below the peak.


I systematically  add the noise , but the , um , noise level is just  some kind of threshold below the peak.


Um. Yeah. Which is not really noise , actually. It 's just adding a constant to each of the mel , uh , energy.


So , yeah , it 's really , uh , white noise. I th


So , yeah , it 's really , uh , white noise. I th

So then afterwards a log is taken , and that 's so sort of why the   the little variation tends to go away.


So then afterwards a log is taken , and that 's so sort of why the   the little variation tends to go away.

Mm - hmm. Um. Yeah. So may Well , the  this threshold is still a factor that we have to look at. And I don't know , maybe a constant noise addition would   would be fine also , or  Um


So then afterwards a log is taken , and that 's so sort of why the   the little variation tends to go away.

Mm - hmm. Um. Yeah. So may Well , the  this threshold is still a factor that we have to look at. And I don't know , maybe a constant noise addition would   would be fine also , or  Um

Or  or not constant but  but , uh , varying over time  in fact is another way  to go.


Mm - hmm. Um. Yeah. So may Well , the  this threshold is still a factor that we have to look at. And I don't know , maybe a constant noise addition would   would be fine also , or  Um

Or  or not constant but  but , uh , varying over time  in fact is another way  to go.


Or  or not constant but  but , uh , varying over time  in fact is another way  to go.


Were you using the  the normalization in addition to this ? I mean , what was the rest of the system ?


Were you using the  the normalization in addition to this ? I mean , what was the rest of the system ?

Um  Yeah. It was  it was , uh , the same system. Mm - hmm.


Were you using the  the normalization in addition to this ? I mean , what was the rest of the system ?

Um  Yeah. It was  it was , uh , the same system. Mm - hmm.

It was the same system. Mmm. Oh , yeah. A third thing is that , um ,  I play a little bit with the , um   finding what was different between , um , And there were a couple of differences , like the LDA filters were not the same. Um , he had the France Telecom blind equalization in the system. Um , the number o of MFCC that was  were used was different. You used thirteen and we used fifteen. Well , a bunch of differences. And , um , actually the result that he  he got were much better on TI - digits especially. So I 'm kind of investigated to see what was the main factor for this difference. And it seems that the LDA filter is  is  was hurting. Um ,  so when we put s some noise compensation the , um , LDA filter that  that 's derived from noisy speech is not more  anymore optimal. And it makes a big difference , um ,  on TI - digits trained on clean. Uh , if we use the  the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate , um , on noisy speech when the system is trained on clean speech. But  and when we use the filter that 's derived from clean speech we jumped  so from eighty - two point seven to eighty - five point one , which is a huge leap.


Um  Yeah. It was  it was , uh , the same system. Mm - hmm.

It was the same system. Mmm. Oh , yeah. A third thing is that , um ,  I play a little bit with the , um   finding what was different between , um , And there were a couple of differences , like the LDA filters were not the same. Um , he had the France Telecom blind equalization in the system. Um , the number o of MFCC that was  were used was different. You used thirteen and we used fifteen. Well , a bunch of differences. And , um , actually the result that he  he got were much better on TI - digits especially. So I 'm kind of investigated to see what was the main factor for this difference. And it seems that the LDA filter is  is  was hurting. Um ,  so when we put s some noise compensation the , um , LDA filter that  that 's derived from noisy speech is not more  anymore optimal. And it makes a big difference , um ,  on TI - digits trained on clean. Uh , if we use the  the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate , um , on noisy speech when the system is trained on clean speech. But  and when we use the filter that 's derived from clean speech we jumped  so from eighty - two point seven to eighty - five point one , which is a huge leap.


It was the same system. Mmm. Oh , yeah. A third thing is that , um ,  I play a little bit with the , um   finding what was different between , um , And there were a couple of differences , like the LDA filters were not the same. Um , he had the France Telecom blind equalization in the system. Um , the number o of MFCC that was  were used was different. You used thirteen and we used fifteen. Well , a bunch of differences. And , um , actually the result that he  he got were much better on TI - digits especially. So I 'm kind of investigated to see what was the main factor for this difference. And it seems that the LDA filter is  is  was hurting. Um ,  so when we put s some noise compensation the , um , LDA filter that  that 's derived from noisy speech is not more  anymore optimal. And it makes a big difference , um ,  on TI - digits trained on clean. Uh , if we use the  the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate , um , on noisy speech when the system is trained on clean speech. But  and when we use the filter that 's derived from clean speech we jumped  so from eighty - two point seven to eighty - five point one , which is a huge leap.

Um. Yeah. So now the results are more similar , and I don't  I will not , I think , investigate on the other differences , which is like the number of MFCC that we keep and other small things that we can I think optimize later on anyway.


It was the same system. Mmm. Oh , yeah. A third thing is that , um ,  I play a little bit with the , um   finding what was different between , um , And there were a couple of differences , like the LDA filters were not the same. Um , he had the France Telecom blind equalization in the system. Um , the number o of MFCC that was  were used was different. You used thirteen and we used fifteen. Well , a bunch of differences. And , um , actually the result that he  he got were much better on TI - digits especially. So I 'm kind of investigated to see what was the main factor for this difference. And it seems that the LDA filter is  is  was hurting. Um ,  so when we put s some noise compensation the , um , LDA filter that  that 's derived from noisy speech is not more  anymore optimal. And it makes a big difference , um ,  on TI - digits trained on clean. Uh , if we use the  the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate , um , on noisy speech when the system is trained on clean speech. But  and when we use the filter that 's derived from clean speech we jumped  so from eighty - two point seven to eighty - five point one , which is a huge leap.

Um. Yeah. So now the results are more similar , and I don't  I will not , I think , investigate on the other differences , which is like the number of MFCC that we keep and other small things that we can I think optimize later on anyway.

Sure. But on the other hand if everybody is trying different kinds of noise suppression things and so forth , it might be good to standardize on the piece  that we 're not changing. Right ? So if there 's any particular reason to ha pick one or the other , I mean  Which  which one is closer to what the proposal was that was submitted to Aurora ? Are they  they both  ? Well , I mean


Um. Yeah. So now the results are more similar , and I don't  I will not , I think , investigate on the other differences , which is like the number of MFCC that we keep and other small things that we can I think optimize later on anyway.

Sure. But on the other hand if everybody is trying different kinds of noise suppression things and so forth , it might be good to standardize on the piece  that we 're not changing. Right ? So if there 's any particular reason to ha pick one or the other , I mean  Which  which one is closer to what the proposal was that was submitted to Aurora ? Are they  they both  ? Well , I mean

I think  Yeah. I think th th uh , the new system that I tested is , I guess , closer because it doesn't have  it have less of  of France Telecom stuff ,


Sure. But on the other hand if everybody is trying different kinds of noise suppression things and so forth , it might be good to standardize on the piece  that we 're not changing. Right ? So if there 's any particular reason to ha pick one or the other , I mean  Which  which one is closer to what the proposal was that was submitted to Aurora ? Are they  they both  ? Well , I mean

I think  Yeah. I think th th uh , the new system that I tested is , I guess , closer because it doesn't have  it have less of  of France Telecom stuff ,

The  whatever you , uh , tested with recently. Right ?


I think  Yeah. I think th th uh , the new system that I tested is , I guess , closer because it doesn't have  it have less of  of France Telecom stuff ,

The  whatever you , uh , tested with recently. Right ?


The  whatever you , uh , tested with recently. Right ?


The  whatever you , uh , tested with recently. Right ?

Well , no , I  I 'm  I  Yeah , you 're trying to add in France Telecom.


Well , no , I  I 'm  I  Yeah , you 're trying to add in France Telecom.


Well , no , I  I 'm  I  Yeah , you 're trying to add in France Telecom.

Tell them about the rest of it. Like you said the number of filters might be  different or something. Right ? Or


Well , no , I  I 'm  I  Yeah , you 're trying to add in France Telecom.

Tell them about the rest of it. Like you said the number of filters might be  different or something. Right ? Or

The number of cepstral coefficients is what ?


Tell them about the rest of it. Like you said the number of filters might be  different or something. Right ? Or

The number of cepstral coefficients is what ?


The number of cepstral coefficients is what ?

Yeah. So , I mean , I think we 'd wanna standardize there , wouldn't we ?


Yeah. So , I mean , I think we 'd wanna standardize there , wouldn't we ?


I think we were gonna work with  with this or this new system , or with


I think we were gonna work with  with this or this new system , or with

Uh , so the  the  right now , the  the system that is there in the  what we have in the repositories , with  uses fifteen.


Uh , so the  the  right now , the  the system that is there in the  what we have in the repositories , with  uses fifteen.

But we will use the  the LDA filters f derived from clean speech. Well , yeah , actually it 's  it 's not the  the LDA filter.


But we will use the  the LDA filters f derived from clean speech. Well , yeah , actually it 's  it 's not the  the LDA filter.


But we will use the  the LDA filters f derived from clean speech. Well , yeah , actually it 's  it 's not the  the LDA filter.

It 's something that 's also short enough in  in latency.


It 's something that 's also short enough in  in latency.


It 's something that 's also short enough in  in latency.

Yeah. So , we haven't  w we have been always using , uh , fifteen coefficients ,


Yeah. So , we haven't  w we have been always using , uh , fifteen coefficients ,


Yeah. Well , uh , that 's  something 's  Um. Yeah. Then


Yeah. Well , uh , that 's  something 's  Um. Yeah. Then

I think as long as you guys agree on it , it doesn't matter.


Yeah. Well , uh , that 's  something 's  Um. Yeah. Then

I think as long as you guys agree on it , it doesn't matter.

I think we have a maximum of sixty ,  uh , features that we 're allowed. So.


I think as long as you guys agree on it , it doesn't matter.

I think we have a maximum of sixty ,  uh , features that we 're allowed. So.


I think we have a maximum of sixty ,  uh , features that we 're allowed. So.

Yeah. Ma - maybe we can  I mean , at least , um , I 'll t s run some experiments to see whether  once I have this   noise compensation to see whether thirteen and fifteen really matters or not.


Yeah. Ma - maybe we can  I mean , at least , um , I 'll t s run some experiments to see whether  once I have this   noise compensation to see whether thirteen and fifteen really matters or not.

Never tested it with the compensation , but without ,  uh , compensation it was like fifteen was s slightly better than thirteen ,


Never tested it with the compensation , but without ,  uh , compensation it was like fifteen was s slightly better than thirteen ,


Never tested it with the compensation , but without ,  uh , compensation it was like fifteen was s slightly better than thirteen ,

Yeah. And there is  there is also this log energy versus C - zero.


Yeah. And there is  there is also this log energy versus C - zero.

Sorry , fifteen. Yeah , the log energy versus C - zero.


Yeah. And there is  there is also this log energy versus C - zero.

Sorry , fifteen. Yeah , the log energy versus C - zero.

Uh , that 's  that 's the other thing. I mean , without noise compensation certainly C - zero is better than log energy. Be - I mean , because the  there are more , uh , mismatched conditions than the matching conditions for testing.


Sorry , fifteen. Yeah , the log energy versus C - zero.

Uh , that 's  that 's the other thing. I mean , without noise compensation certainly C - zero is better than log energy. Be - I mean , because the  there are more , uh , mismatched conditions than the matching conditions for testing.


Uh , that 's  that 's the other thing. I mean , without noise compensation certainly C - zero is better than log energy. Be - I mean , because the  there are more , uh , mismatched conditions than the matching conditions for testing.

You know , always for the matched condition , you always get a  slightly better performance for log energy than C - zero.


You know , always for the matched condition , you always get a  slightly better performance for log energy than C - zero.

But not for  I mean , for matched and the clean condition both , you get log energy  I mean you get a better performance with log energy.


But not for  I mean , for matched and the clean condition both , you get log energy  I mean you get a better performance with log energy.

Well , um , maybe once we have this noise compensation , I don't know , we have to try that also , whether we want to go for C - zero or log energy.


Well , um , maybe once we have this noise compensation , I don't know , we have to try that also , whether we want to go for C - zero or log energy.


Uh , no. I 'm just , you know , being a manager this week. So.


Uh , no. I 'm just , you know , being a manager this week. So.

Um ,  still working on my  my quals preparation stuff. Um ,  so I 'm  I 'm thinking about , um , starting some ,  uh , cheating experiments to , uh , determine the , um   the relative effectiveness of , um , some intermediate categories that I want to classify. So , for example , um ,  if I know where voicing occurs and everything , um ,  I would do a phone  um , phone recognition experiment , um , somehow putting in the  the , uh  the perfect knowledge that I have about voicing. So , um , in particular I was thinking ,  um , in  in the hybrid framework , just taking those LNA files ,  and , um ,  setting to zero those probabilities that , um  that these phones are not voicing. So say , like , I know this particular segment is voicing , um ,  I would say , uh , go into the corresponding LNA file and zonk out the  the posteriors for , um , those phonemes that , um , are not voiced ,


Um ,  still working on my  my quals preparation stuff. Um ,  so I 'm  I 'm thinking about , um , starting some ,  uh , cheating experiments to , uh , determine the , um   the relative effectiveness of , um , some intermediate categories that I want to classify. So , for example , um ,  if I know where voicing occurs and everything , um ,  I would do a phone  um , phone recognition experiment , um , somehow putting in the  the , uh  the perfect knowledge that I have about voicing. So , um , in particular I was thinking ,  um , in  in the hybrid framework , just taking those LNA files ,  and , um ,  setting to zero those probabilities that , um  that these phones are not voicing. So say , like , I know this particular segment is voicing , um ,  I would say , uh , go into the corresponding LNA file and zonk out the  the posteriors for , um , those phonemes that , um , are not voiced ,

and then see what kinds of improvements I get. And so this would be a useful thing , um , to know  in terms of , like , which  which , um  which of these categories are  are good for , um , speech recognition.


and then see what kinds of improvements I get. And so this would be a useful thing , um , to know  in terms of , like , which  which , um  which of these categories are  are good for , um , speech recognition.

So , that 's  I hope to get those , uh  those experiments done by  by the time quals come  come around in July.


and then see what kinds of improvements I get. And so this would be a useful thing , um , to know  in terms of , like , which  which , um  which of these categories are  are good for , um , speech recognition.

So , that 's  I hope to get those , uh  those experiments done by  by the time quals come  come around in July.

So do you just take the probabilities of the other ones and spread them out evenly among the  the remaining ones ?


So , that 's  I hope to get those , uh  those experiments done by  by the time quals come  come around in July.

So do you just take the probabilities of the other ones and spread them out evenly among the  the remaining ones ?

Yeah. I  I  I was thinking  OK , so just set to  set to some really low number , the  the non - voiced , um , phones.


So do you just take the probabilities of the other ones and spread them out evenly among the  the remaining ones ?

Yeah. I  I  I was thinking  OK , so just set to  set to some really low number , the  the non - voiced , um , phones.


Yeah. I  I  I was thinking  OK , so just set to  set to some really low number , the  the non - voiced , um , phones.


Cool. That will be really interesting to see , you know. So then you 're gonna feed the  those into  some standard recognizer.


Cool. That will be really interesting to see , you know. So then you 're gonna feed the  those into  some standard recognizer.

Yeah , m Um , well , I 'm gonna f work with TIMIT


Yeah , m Um , well , I 'm gonna f work with TIMIT


Yeah , m Um , well , I 'm gonna f work with TIMIT

TIMIT  uh , phone recognition with TIMIT.


TIMIT  uh , phone recognition with TIMIT.


TIMIT  uh , phone recognition with TIMIT.

Oh , so then you 'll feed those  Sorry. So where do the outputs of the net go into if you 're doing phone recognition ?


Oh , so then you 'll feed those  Sorry. So where do the outputs of the net go into if you 're doing phone recognition ?

Oh. Um , the outputs of the net go into the standard , h um , ICSI hybrid , um , recognizer. So maybe , um , Chronos


Oh , so then you 'll feed those  Sorry. So where do the outputs of the net go into if you 're doing phone recognition ?

Oh. Um , the outputs of the net go into the standard , h um , ICSI hybrid , um , recognizer. So maybe , um , Chronos

An - and you 're gonna  the  you 're gonna do phone recognition with that ?


Oh. Um , the outputs of the net go into the standard , h um , ICSI hybrid , um , recognizer. So maybe , um , Chronos

An - and you 're gonna  the  you 're gonna do phone recognition with that ?


An - and you 're gonna  the  you 're gonna do phone recognition with that ?

So. And , uh , another thing would be to extend this to , uh , digits or something where I can look at whole words.


So. And , uh , another thing would be to extend this to , uh , digits or something where I can look at whole words.


So. And , uh , another thing would be to extend this to , uh , digits or something where I can look at whole words.

And I would be able to see , uh , not just , like , phoneme events , but , um ,  inter - phoneme events. So , like , this is from a stop to  to a vo a vocalic


And I would be able to see , uh , not just , like , phoneme events , but , um ,  inter - phoneme events. So , like , this is from a stop to  to a vo a vocalic

segment. You know , so something that is transitional in nature.


segment. You know , so something that is transitional in nature.


Um  Let 's see , I haven't done a whole lot on anything related to this this week. I 've been focusing mainly on Meeting Recorder stuff.


Um  Let 's see , I haven't done a whole lot on anything related to this this week. I 've been focusing mainly on Meeting Recorder stuff.

So , um ,  I guess I 'll just pass it on to Dave.


Um  Let 's see , I haven't done a whole lot on anything related to this this week. I 've been focusing mainly on Meeting Recorder stuff.

So , um ,  I guess I 'll just pass it on to Dave.

Uh , OK. Well , in my lunch talk last week I  I said I 'd tried phase normalization and gotten garbage results using that l um , long - term mean subtraction approach. It turned out there was a bug in my Matlab code. So I tried it again , um , and , um , the results  were  were better. I got intelligible speech back. But they still weren't as good as just subtracting the magnitude  the log magnitude means. And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model and about coming up with a good model for , um , far mike use of the SmartKom system. So I 'm gonna be working on , um , implementing this mean subtraction approach in the  far - mike system  for the SmartKom system , I mean. And , um , one of the experiments we 're gonna do is , um , we 're gonna , um , train the  a Broadcast News net , which is because that 's what we 've been using so far , and , um , adapt it on some other data. Um , An - Andreas wants to use , um , data that resembles read speech , like  these digit readings , because he feels that the SmartKom system interaction is not gonna be exactly conversational.


So , um ,  I guess I 'll just pass it on to Dave.

Uh , OK. Well , in my lunch talk last week I  I said I 'd tried phase normalization and gotten garbage results using that l um , long - term mean subtraction approach. It turned out there was a bug in my Matlab code. So I tried it again , um , and , um , the results  were  were better. I got intelligible speech back. But they still weren't as good as just subtracting the magnitude  the log magnitude means. And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model and about coming up with a good model for , um , far mike use of the SmartKom system. So I 'm gonna be working on , um , implementing this mean subtraction approach in the  far - mike system  for the SmartKom system , I mean. And , um , one of the experiments we 're gonna do is , um , we 're gonna , um , train the  a Broadcast News net , which is because that 's what we 've been using so far , and , um , adapt it on some other data. Um , An - Andreas wants to use , um , data that resembles read speech , like  these digit readings , because he feels that the SmartKom system interaction is not gonna be exactly conversational.


So , um ,  I guess I 'll just pass it on to Dave.

Uh , OK. Well , in my lunch talk last week I  I said I 'd tried phase normalization and gotten garbage results using that l um , long - term mean subtraction approach. It turned out there was a bug in my Matlab code. So I tried it again , um , and , um , the results  were  were better. I got intelligible speech back. But they still weren't as good as just subtracting the magnitude  the log magnitude means. And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model and about coming up with a good model for , um , far mike use of the SmartKom system. So I 'm gonna be working on , um , implementing this mean subtraction approach in the  far - mike system  for the SmartKom system , I mean. And , um , one of the experiments we 're gonna do is , um , we 're gonna , um , train the  a Broadcast News net , which is because that 's what we 've been using so far , and , um , adapt it on some other data. Um , An - Andreas wants to use , um , data that resembles read speech , like  these digit readings , because he feels that the SmartKom system interaction is not gonna be exactly conversational.

S so actually I was wondering , how long does it take to train that Broadcast News net ?


Uh , OK. Well , in my lunch talk last week I  I said I 'd tried phase normalization and gotten garbage results using that l um , long - term mean subtraction approach. It turned out there was a bug in my Matlab code. So I tried it again , um , and , um , the results  were  were better. I got intelligible speech back. But they still weren't as good as just subtracting the magnitude  the log magnitude means. And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model and about coming up with a good model for , um , far mike use of the SmartKom system. So I 'm gonna be working on , um , implementing this mean subtraction approach in the  far - mike system  for the SmartKom system , I mean. And , um , one of the experiments we 're gonna do is , um , we 're gonna , um , train the  a Broadcast News net , which is because that 's what we 've been using so far , and , um , adapt it on some other data. Um , An - Andreas wants to use , um , data that resembles read speech , like  these digit readings , because he feels that the SmartKom system interaction is not gonna be exactly conversational.

S so actually I was wondering , how long does it take to train that Broadcast News net ?

The big one takes a while. Yeah. That takes two , three weeks.


S so actually I was wondering , how long does it take to train that Broadcast News net ?

The big one takes a while. Yeah. That takes two , three weeks.


S so actually I was wondering , how long does it take to train that Broadcast News net ?

The big one takes a while. Yeah. That takes two , three weeks.

So  but , you know , uh , you can get  I don't know if you even want to run the big one , uh , um , in the  in the final system , cuz , you know , it takes a little while to run it. So ,  um , you can scale it down by  I 'm sorry , it was two , three weeks for training up for the large Broadcast News test set  training set. I don't know how much you 'd be training on.


The big one takes a while. Yeah. That takes two , three weeks.

So  but , you know , uh , you can get  I don't know if you even want to run the big one , uh , um , in the  in the final system , cuz , you know , it takes a little while to run it. So ,  um , you can scale it down by  I 'm sorry , it was two , three weeks for training up for the large Broadcast News test set  training set. I don't know how much you 'd be training on.


So  but , you know , uh , you can get  I don't know if you even want to run the big one , uh , um , in the  in the final system , cuz , you know , it takes a little while to run it. So ,  um , you can scale it down by  I 'm sorry , it was two , three weeks for training up for the large Broadcast News test set  training set. I don't know how much you 'd be training on.


Uh , i so if you trained on half as much  and made the net , uh , uh , half as big , then it would be one fourth  the amount of time


Yeah. Also , I guess we had  we 've had these , uh , little di discussions  I guess you ha haven't had a chance to work with it too much  about  about , uh  uh , uh m other ways of taking care of the phase. So , I mean , I  I guess that was something I could say would be that we 've talked a little bit about


Yeah. Also , I guess we had  we 've had these , uh , little di discussions  I guess you ha haven't had a chance to work with it too much  about  about , uh  uh , uh m other ways of taking care of the phase. So , I mean , I  I guess that was something I could say would be that we 've talked a little bit about

you just doing it all with complex arithmetic and , uh  and not  not , uh , doing the polar representation with magnitude and phase. But  it looks like there 's ways that one could potentially just work with the complex numbers and  and  and in principle get rid of the  effects of the average complex spectrum. But


Yeah. Also , I guess we had  we 've had these , uh , little di discussions  I guess you ha haven't had a chance to work with it too much  about  about , uh  uh , uh m other ways of taking care of the phase. So , I mean , I  I guess that was something I could say would be that we 've talked a little bit about

you just doing it all with complex arithmetic and , uh  and not  not , uh , doing the polar representation with magnitude and phase. But  it looks like there 's ways that one could potentially just work with the complex numbers and  and  and in principle get rid of the  effects of the average complex spectrum. But

And , um , actually , regarding the phase normalization  So I did two experiments , and one is  So , phases get added , modulo two pi , and  because you only know the phase of the complex number t t to a value modulo two pi. And so I thought at first , um , that , uh , what I should do is unwrap the phase because that will undo that. Um , but I actually got worse results doing that unwrapping using the simple phase unwrapper that 's in Matlab than I did not unwrapping at all.


you just doing it all with complex arithmetic and , uh  and not  not , uh , doing the polar representation with magnitude and phase. But  it looks like there 's ways that one could potentially just work with the complex numbers and  and  and in principle get rid of the  effects of the average complex spectrum. But

And , um , actually , regarding the phase normalization  So I did two experiments , and one is  So , phases get added , modulo two pi , and  because you only know the phase of the complex number t t to a value modulo two pi. And so I thought at first , um , that , uh , what I should do is unwrap the phase because that will undo that. Um , but I actually got worse results doing that unwrapping using the simple phase unwrapper that 's in Matlab than I did not unwrapping at all.


And , um , actually , regarding the phase normalization  So I did two experiments , and one is  So , phases get added , modulo two pi , and  because you only know the phase of the complex number t t to a value modulo two pi. And so I thought at first , um , that , uh , what I should do is unwrap the phase because that will undo that. Um , but I actually got worse results doing that unwrapping using the simple phase unwrapper that 's in Matlab than I did not unwrapping at all.


Yeah. So I 'm  I 'm still hopeful that  that  I mean , we  we don't even know if the phase  is something  the average phase is something that we do want to remove. I mean , maybe there 's some deeper reason why it isn't the right thing to do. But , um , at least in principle it looks like there 's  there 's , uh , a couple potential ways to do it. One  one being to just work with the complex numbers , um , and , uh  in rectangular kind of coordinates. And the other is  to , uh , do a Taylor series  Well. So you work with the complex numbers and then when you get the spectrum  the average complex spectrum  um , actually divide it out , um , as opposed to taking the log and subtracting. So then , um , um , you know , there might be some numerical issues. We don't really know that. The other thing we talked a little bit about was Taylor series expansion. And , um , uh , actually I was talking to Dick Karp about it a little bit , and  and  and , since I got thinking about it , and  and , uh , so one thing is that y you 'd have to do , I think , uh  we may have to do this on a whiteboard , but I think you have to be a little careful about scaling the numbers that you 're  taking  the complex numbers that you 're taking the log of because  the Taylor expansion for it has , you know , a square and a cube , and  and so forth. And  and so if   if you have a  a number that is modulus , you know , uh , very different from one   It should be right around one , if it 's  cuz it 's a expansion of log one  one minus epsilon or o is  is  one plus epsilon , or is it one plus  ? Well , there 's an epsilon squared over two and an epsilon cubed over three ,


Yeah. So I 'm  I 'm still hopeful that  that  I mean , we  we don't even know if the phase  is something  the average phase is something that we do want to remove. I mean , maybe there 's some deeper reason why it isn't the right thing to do. But , um , at least in principle it looks like there 's  there 's , uh , a couple potential ways to do it. One  one being to just work with the complex numbers , um , and , uh  in rectangular kind of coordinates. And the other is  to , uh , do a Taylor series  Well. So you work with the complex numbers and then when you get the spectrum  the average complex spectrum  um , actually divide it out , um , as opposed to taking the log and subtracting. So then , um , um , you know , there might be some numerical issues. We don't really know that. The other thing we talked a little bit about was Taylor series expansion. And , um , uh , actually I was talking to Dick Karp about it a little bit , and  and  and , since I got thinking about it , and  and , uh , so one thing is that y you 'd have to do , I think , uh  we may have to do this on a whiteboard , but I think you have to be a little careful about scaling the numbers that you 're  taking  the complex numbers that you 're taking the log of because  the Taylor expansion for it has , you know , a square and a cube , and  and so forth. And  and so if   if you have a  a number that is modulus , you know , uh , very different from one   It should be right around one , if it 's  cuz it 's a expansion of log one  one minus epsilon or o is  is  one plus epsilon , or is it one plus  ? Well , there 's an epsilon squared over two and an epsilon cubed over three ,

and so forth. So if epsilon is bigger than one , then it diverges.


and so forth. So if epsilon is bigger than one , then it diverges.

So you have to do some scaling. But that 's not a big deal cuz it 's the log of   of K times a complex number , then you can just  that 's the same as log of K plus  log of the complex number.


So you have to do some scaling. But that 's not a big deal cuz it 's the log of   of K times a complex number , then you can just  that 's the same as log of K plus  log of the complex number.


So , um , I 've been , uh , implementing this , uh , Wiener filtering for this Aurora task. And , uh , I  I actually thought it was  it was doing fine when I tested it once. I it 's , like , using a small section of the code. And then I ran the whole recognition experiment with Italian and I got ,  like , worse results than not using it. Then I  So , I 've been trying to find where the problem came from. And then it looks like I have some problem in the way  there is some  some very silly bug somewhere. And , ugh ! I  I mean , i uh , it actually  i it actually made the whole thing worse. I was looking at the spectrograms that I got and it 's , like  w it 's  it 's very horrible. Like , when I


So , um , I 've been , uh , implementing this , uh , Wiener filtering for this Aurora task. And , uh , I  I actually thought it was  it was doing fine when I tested it once. I it 's , like , using a small section of the code. And then I ran the whole recognition experiment with Italian and I got ,  like , worse results than not using it. Then I  So , I 've been trying to find where the problem came from. And then it looks like I have some problem in the way  there is some  some very silly bug somewhere. And , ugh ! I  I mean , i uh , it actually  i it actually made the whole thing worse. I was looking at the spectrograms that I got and it 's , like  w it 's  it 's very horrible. Like , when I

I  I missed the v I 'm sorry , I was  I was distracted. I missed the very first sentence. So then , I 'm a little lost on the rest.


I  I missed the v I 'm sorry , I was  I was distracted. I missed the very first sentence. So then , I 'm a little lost on the rest.

Oh , yeah. I actually implemented the Wiener f f fil filtering as a module and then tested it out separately.


Oh , yeah. I actually implemented the Wiener f f fil filtering as a module and then tested it out separately.


Oh , yeah. I actually implemented the Wiener f f fil filtering as a module and then tested it out separately.

And it  it  it gave , like  I just got the signal out and it  it was OK. So , I plugged it in somewhere and then  I mean , it 's like I had to remove some part and then plugging it in somewhere. And then I  in that process I messed it up somewhere.


And it  it  it gave , like  I just got the signal out and it  it was OK. So , I plugged it in somewhere and then  I mean , it 's like I had to remove some part and then plugging it in somewhere. And then I  in that process I messed it up somewhere.

So. So , it was real I mean , I thought it was all fine and then I ran it , and I got something worse than not using it. So , I was like  I 'm trying to find where the m m problem came ,


So. So , it was real I mean , I thought it was all fine and then I ran it , and I got something worse than not using it. So , I was like  I 'm trying to find where the m m problem came ,


some silly stuff. And , um , the other thing , uh , was , uh , uh  Well , Hynek showed up one  suddenly on one day and then I was t talking wi


some silly stuff. And , um , the other thing , uh , was , uh , uh  Well , Hynek showed up one  suddenly on one day and then I was t talking wi

Right. Yeah. As  as he is wont to do. Yeah.


some silly stuff. And , um , the other thing , uh , was , uh , uh  Well , Hynek showed up one  suddenly on one day and then I was t talking wi

Right. Yeah. As  as he is wont to do. Yeah.

Uh , yeah. So I was actually  that day I was thinking about d doing something about the Wiener filtering , and then Carlos matter of stuff. And then he showed up and then I told him. And then he gave me a whole bunch of filters  what Carlos used for his , uh , uh , thesis and then  that was something which came up. And then , um  So , uh , I 'm actually ,  uh , thinking of using that also in this , uh , W Wiener filtering because that is a m modified Wiener filtering approach , where instead of using the current frame , it uses  adjacent frames also in designing the Wiener filter. So instead of designing our own new Wiener filters , I may just use one of those Carlos filters in  in this implementation


Right. Yeah. As  as he is wont to do. Yeah.

Uh , yeah. So I was actually  that day I was thinking about d doing something about the Wiener filtering , and then Carlos matter of stuff. And then he showed up and then I told him. And then he gave me a whole bunch of filters  what Carlos used for his , uh , uh , thesis and then  that was something which came up. And then , um  So , uh , I 'm actually ,  uh , thinking of using that also in this , uh , W Wiener filtering because that is a m modified Wiener filtering approach , where instead of using the current frame , it uses  adjacent frames also in designing the Wiener filter. So instead of designing our own new Wiener filters , I may just use one of those Carlos filters in  in this implementation

and see whether it  it actually gives me something better than using just the current f current frame , which is in a way , uh , something like the smoothing  the Wiener filter


Uh , yeah. So I was actually  that day I was thinking about d doing something about the Wiener filtering , and then Carlos matter of stuff. And then he showed up and then I told him. And then he gave me a whole bunch of filters  what Carlos used for his , uh , uh , thesis and then  that was something which came up. And then , um  So , uh , I 'm actually ,  uh , thinking of using that also in this , uh , W Wiener filtering because that is a m modified Wiener filtering approach , where instead of using the current frame , it uses  adjacent frames also in designing the Wiener filter. So instead of designing our own new Wiener filters , I may just use one of those Carlos filters in  in this implementation

and see whether it  it actually gives me something better than using just the current f current frame , which is in a way , uh , something like the smoothing  the Wiener filter


and see whether it  it actually gives me something better than using just the current f current frame , which is in a way , uh , something like the smoothing  the Wiener filter

but @ @  S so , I don't know , I was h I 'm  I 'm  I 'm , like  that  so that is the next thing. Once this  I  once I sort this pro uh , problem out maybe I 'll just go into that also. And the  the other thing was about the subspace approach. So , um , I , like , plugged some groupings for computing this eigen uh , uh , uh , s values and eigenvectors. So just  I just @ @ some small block of things which I needed to put together for the subspace approach. And I 'm in the process of , like , building up that stuff. And , um , uh   Yeah. I guess  Yep. I guess that 's it. And , uh , th th that 's where I am right now. So.


but @ @  S so , I don't know , I was h I 'm  I 'm  I 'm , like  that  so that is the next thing. Once this  I  once I sort this pro uh , problem out maybe I 'll just go into that also. And the  the other thing was about the subspace approach. So , um , I , like , plugged some groupings for computing this eigen uh , uh , uh , s values and eigenvectors. So just  I just @ @ some small block of things which I needed to put together for the subspace approach. And I 'm in the process of , like , building up that stuff. And , um , uh   Yeah. I guess  Yep. I guess that 's it. And , uh , th th that 's where I am right now. So.

Mmm. I 'm working with VTS. Um , I do several experiment with the Spanish database first , only with VTS and nothing more. Not VAD , no LDA , nothing more.


Mmm. I 'm working with VTS. Um , I do several experiment with the Spanish database first , only with VTS and nothing more. Not VAD , no LDA , nothing more.


Right , right. I think I ask you that every single meeting , don't I ?


So , that 'd be good from  for analysis.


So , that 'd be good from  for analysis.

It 's good to have some , uh , cases of the same utterance at different  different times.


It 's good to have some , uh , cases of the same utterance at different  different times.


VTS. I 'm sor Well , um , the question is that  Well. Remove some noise but not too much. And when we put the  m m the , em , VAD , the result is better. And we put everything , the result is better , but it 's not better than the result that we have without VTS. No , no.


VTS. I 'm sor Well , um , the question is that  Well. Remove some noise but not too much. And when we put the  m m the , em , VAD , the result is better. And we put everything , the result is better , but it 's not better than the result that we have without VTS. No , no.

I see. So that @ @  given that you 're using the VAD also , the effect of the VTS is not  so far


VTS. I 'm sor Well , um , the question is that  Well. Remove some noise but not too much. And when we put the  m m the , em , VAD , the result is better. And we put everything , the result is better , but it 's not better than the result that we have without VTS. No , no.

I see. So that @ @  given that you 're using the VAD also , the effect of the VTS is not  so far

Do you  How much of that do you think is due to just the particular implementation and how much you 're adjusting it ? Or how much do you think is intrinsic to  ?


I see. So that @ @  given that you 're using the VAD also , the effect of the VTS is not  so far

Do you  How much of that do you think is due to just the particular implementation and how much you 're adjusting it ? Or how much do you think is intrinsic to  ?


Do you  How much of that do you think is due to just the particular implementation and how much you 're adjusting it ? Or how much do you think is intrinsic to  ?

Are you still using only the ten first frame for noise estimation


Are you still using only the ten first frame for noise estimation


Are you still using only the ten first frame for noise estimation

Uh , I do the experiment using only the f onl eh , to use on only one fair estimation of the noise.


Uh , I do the experiment using only the f onl eh , to use on only one fair estimation of the noise.


Uh , I do the experiment using only the f onl eh , to use on only one fair estimation of the noise.

And also I did some experiment ,  uh , doing , um , a lying estimation of the noise. And , well , it 's a little bit better but not  n


Uh , I do the experiment using only the f onl eh , to use on only one fair estimation of the noise.

And also I did some experiment ,  uh , doing , um , a lying estimation of the noise. And , well , it 's a little bit better but not  n

Maybe you have to standardize this thing also , noise estimation , because all the thing that you are testing use a different  They all need some  some noise  noise spectra


And also I did some experiment ,  uh , doing , um , a lying estimation of the noise. And , well , it 's a little bit better but not  n

Maybe you have to standardize this thing also , noise estimation , because all the thing that you are testing use a different  They all need some  some noise  noise spectra


And also I did some experiment ,  uh , doing , um , a lying estimation of the noise. And , well , it 's a little bit better but not  n

Maybe you have to standardize this thing also , noise estimation , because all the thing that you are testing use a different  They all need some  some noise  noise spectra

Mmm. No , I do that two  t did two time.


Maybe you have to standardize this thing also , noise estimation , because all the thing that you are testing use a different  They all need some  some noise  noise spectra

Mmm. No , I do that two  t did two time.

but they use  every  all use a different one.


Mmm. No , I do that two  t did two time.

but they use  every  all use a different one.

I have an idea. If  if , uh , uh , y you 're right. I mean , each of these require this. Um , given that we 're going to have for this test at least of  uh , boundaries , what if initially we start off by using  known sections of nonspeech  for the estimation ?


but they use  every  all use a different one.

I have an idea. If  if , uh , uh , y you 're right. I mean , each of these require this. Um , given that we 're going to have for this test at least of  uh , boundaries , what if initially we start off by using  known sections of nonspeech  for the estimation ?


I have an idea. If  if , uh , uh , y you 're right. I mean , each of these require this. Um , given that we 're going to have for this test at least of  uh , boundaries , what if initially we start off by using  known sections of nonspeech  for the estimation ?


first place , I mean even if ultimately we wouldn't be given the boundaries ,  uh , this would be a good initial experiment to separate out the effects of things. I mean , how much is the poor   you know , relatively , uh , unhelpful result that you 're getting in this or this or this is due to some inherent limitation to the method for these tasks and how much of it is just due to the fact that you 're not accurately  finding enough regions that  that are really  n noise ?


Um. So maybe if you tested it using that ,  you 'd have more reliable  stretches of nonspeech to do the estimation from and see if that helps.


Um. So maybe if you tested it using that ,  you 'd have more reliable  stretches of nonspeech to do the estimation from and see if that helps.

Yeah. Another thing is the , em  the codebook , the initial codebook. That maybe , well , it 's too clean and


Um. So maybe if you tested it using that ,  you 'd have more reliable  stretches of nonspeech to do the estimation from and see if that helps.

Yeah. Another thing is the , em  the codebook , the initial codebook. That maybe , well , it 's too clean and

Cuz it 's a  I don't know. The methods  If you want , you c I can say something about the method.


Yeah. Another thing is the , em  the codebook , the initial codebook. That maybe , well , it 's too clean and

Cuz it 's a  I don't know. The methods  If you want , you c I can say something about the method.


Cuz it 's a  I don't know. The methods  If you want , you c I can say something about the method.

Yeah. In the  Because it 's  a little bit different of the other method. Well , we have  If this  if this is the noise signal ,  uh , in the log domain , we have something like this. Now , we have something like this. And the idea of these methods is to   n given a , um


Cuz it 's a  I don't know. The methods  If you want , you c I can say something about the method.

Yeah. In the  Because it 's  a little bit different of the other method. Well , we have  If this  if this is the noise signal ,  uh , in the log domain , we have something like this. Now , we have something like this. And the idea of these methods is to   n given a , um

How do you say ? I will read because it 's better for my English. I i given is the estimate of the PDF of the noise signal when we have a , um , a statistic of the clean speech and an statistic of the noisy speech. And the clean speech  the statistic of the clean speech is  from a  codebook. Mmm ? This is the idea. Well , like , this relation is not linear. The methods propose to develop this in a vectorial Taylor series  approximation.


Yeah. In the  Because it 's  a little bit different of the other method. Well , we have  If this  if this is the noise signal ,  uh , in the log domain , we have something like this. Now , we have something like this. And the idea of these methods is to   n given a , um

How do you say ? I will read because it 's better for my English. I i given is the estimate of the PDF of the noise signal when we have a , um , a statistic of the clean speech and an statistic of the noisy speech. And the clean speech  the statistic of the clean speech is  from a  codebook. Mmm ? This is the idea. Well , like , this relation is not linear. The methods propose to develop this in a vectorial Taylor series  approximation.

I I 'm actually just confused about  the equations you have up there. So , uh , the top equation is  is  is


Yeah. In the  Because it 's  a little bit different of the other method. Well , we have  If this  if this is the noise signal ,  uh , in the log domain , we have something like this. Now , we have something like this. And the idea of these methods is to   n given a , um

How do you say ? I will read because it 's better for my English. I i given is the estimate of the PDF of the noise signal when we have a , um , a statistic of the clean speech and an statistic of the noisy speech. And the clean speech  the statistic of the clean speech is  from a  codebook. Mmm ? This is the idea. Well , like , this relation is not linear. The methods propose to develop this in a vectorial Taylor series  approximation.

I I 'm actually just confused about  the equations you have up there. So , uh , the top equation is  is  is

No , this in the  it 's  this is the log domain. I  I must to say that.


How do you say ? I will read because it 's better for my English. I i given is the estimate of the PDF of the noise signal when we have a , um , a statistic of the clean speech and an statistic of the noisy speech. And the clean speech  the statistic of the clean speech is  from a  codebook. Mmm ? This is the idea. Well , like , this relation is not linear. The methods propose to develop this in a vectorial Taylor series  approximation.

I I 'm actually just confused about  the equations you have up there. So , uh , the top equation is  is  is

No , this in the  it 's  this is the log domain. I  I must to say that.


I I 'm actually just confused about  the equations you have up there. So , uh , the top equation is  is  is

No , this in the  it 's  this is the log domain. I  I must to say that.

Is the T  is egual   is equal to , uh , log of


No , this in the  it 's  this is the log domain. I  I must to say that.

Is the T  is egual   is equal to , uh , log of


Is the T  is egual   is equal to , uh , log of


Yeah. I guess it 's the power spectrum of noisy speech.


Yeah , OK. So this  it 's the magnitude squared or something.


Yeah , OK. So this  it 's the magnitude squared or something.

OK , so you have power spectrum added there and down here you have   you  you put the  depends on T , but  b all of this is just  you just mean


OK , so you have power spectrum added there and down here you have   you  you put the  depends on T , but  b all of this is just  you just mean

you just mean the log of the  of the one up above.


you just mean the log of the  of the one up above.


But , n Well , y we can expre we can put this expression


But , n Well , y we can expre we can put this expression

X times one plus , uh , N  uh , N  N  N minus X ?


But , n Well , y we can expre we can put this expression

X times one plus , uh , N  uh , N  N  N minus X ?

And then , uh  So that 's log of X plus log of one plus , uh


X times one plus , uh , N  uh , N  N  N minus X ?

And then , uh  So that 's log of X plus log of one plus , uh


And then , uh  So that 's log of X plus log of one plus , uh


I actually don't see how you get that. Uh.


I actually don't see how you get that. Uh.

Well , if we apply the log , we have E is n


Well , if we apply the log , we have E is n

uh , log   E is equal , oh , to log of X plus N.


uh , log   E is equal , oh , to log of X plus N.


uh , we can say that E   is equal to log of ,   um , exponential of X plus exponential of N.


Well , this is  this is in the ti the time domain. Well , we have that , um  We have first that , for example , X is equal , uh  Well. This is the frequency domain


Well , this is  this is in the ti the time domain. Well , we have that , um  We have first that , for example , X is equal , uh  Well. This is the frequency domain

and we can put  u that n the log domain  log of X omega , but , well , in the time domain we have an exponential. No ? No ? Oh , maybe it 's I am  I 'm problem.


Well , this is  this is in the ti the time domain. Well , we have that , um  We have first that , for example , X is equal , uh  Well. This is the frequency domain

and we can put  u that n the log domain  log of X omega , but , well , in the time domain we have an exponential. No ? No ? Oh , maybe it 's I am  I 'm problem.

Yeah. I mean , just never mind what they are. Uh , it 's just if X and N are variables  Right ?


and we can put  u that n the log domain  log of X omega , but , well , in the time domain we have an exponential. No ? No ? Oh , maybe it 's I am  I 'm problem.

Yeah. I mean , just never mind what they are. Uh , it 's just if X and N are variables  Right ?


and we can put  u that n the log domain  log of X omega , but , well , in the time domain we have an exponential. No ? No ? Oh , maybe it 's I am  I 'm problem.

Yeah. I mean , just never mind what they are. Uh , it 's just if X and N are variables  Right ?

The  the  the log of X plus N is not the same as the log of E to the X plus E to the N.


Yeah. I mean , just never mind what they are. Uh , it 's just if X and N are variables  Right ?

The  the  the log of X plus N is not the same as the log of E to the X plus E to the N.

Yeah. But this i Well , I don't  Well , uh ,


The  the  the log of X plus N is not the same as the log of E to the X plus E to the N.

Yeah. But this i Well , I don't  Well , uh ,


Yeah. But this i Well , I don't  Well , uh ,


I  I can do this incorrectly. Well , the expression that appear in the  in the paper ,  is , uh


I  I can do this incorrectly. Well , the expression that appear in the  in the paper ,  is , uh

The log  the Taylor series expansion for log one plus N by X is


The log  the Taylor series expansion for log one plus N by X is


OK. Yeah. Cuz it doesn't just follow what 's there.


Y yeah. If  if you take log X into log one plus N by X , and then expand the log one plus N by X into Taylor series


Y yeah. If  if you take log X into log one plus N by X , and then expand the log one plus N by X into Taylor series

Yeah , but the  the second  expression that you put is the first - order expansion of the nonlinear relation between


Yeah , but the  the second  expression that you put is the first - order expansion of the nonlinear relation between


Yeah , but the  the second  expression that you put is the first - order expansion of the nonlinear relation between

No , no , no. It 's not the first space. Well , we have  pfft , uh , em  Well , we can put that X is equal  I is equal to log of , uh , mmm


No , no , no. It 's not the first space. Well , we have  pfft , uh , em  Well , we can put that X is equal  I is equal to log of , uh , mmm


That  I mean , that  the f top one does not  imply the second one. Because  cuz the log of a sum is not the same as  th


But we can  uh , we  we know that , for example , the log of  E plus B is equal to log of E plus log to B.


I don't see how you get the second expression from the top one.


I don't see how you get the second expression from the top one.

The  I mean , just more generally here ,  if you say " log of , um , A plus B " , the log of  log of A plus B is not  or A plus B is not the , um , log of E to the A plus E to the B.


I don't see how you get the second expression from the top one.

The  I mean , just more generally here ,  if you say " log of , um , A plus B " , the log of  log of A plus B is not  or A plus B is not the , um , log of E to the A plus E to the B.

No , no , no , no , no , no , no. This not.


The  I mean , just more generally here ,  if you say " log of , um , A plus B " , the log of  log of A plus B is not  or A plus B is not the , um , log of E to the A plus E to the B.

No , no , no , no , no , no , no. This not.

Right ? And that 's what you seem to be saying.


The  I mean , just more generally here ,  if you say " log of , um , A plus B " , the log of  log of A plus B is not  or A plus B is not the , um , log of E to the A plus E to the B.

No , no , no , no , no , no , no. This not.

Right ? And that 's what you seem to be saying.

No. No. It 's not. But this is the same  oh.


No , no , no , no , no , no , no. This not.

Right ? And that 's what you seem to be saying.

No. No. It 's not. But this is the same  oh.

Right ? Cuz you  cuz you  up here you have the A plus B


Right ? And that 's what you seem to be saying.

No. No. It 's not. But this is the same  oh.

Right ? Cuz you  cuz you  up here you have the A plus B

No. I say if I apply log , I have , uh , log of E is equal to log of , uh  in this side , is equal to log of X


No. No. It 's not. But this is the same  oh.

Right ? Cuz you  cuz you  up here you have the A plus B

No. I say if I apply log , I have , uh , log of E is equal to log of , uh  in this side , is equal to log of X


Right ? Cuz you  cuz you  up here you have the A plus B

No. I say if I apply log , I have , uh , log of E is equal to log of , uh  in this side , is equal to log of X


No. I say if I apply log , I have , uh , log of E is equal to log of , uh  in this side , is equal to log of X


And then how do you go from there to the  ?


And then how do you go from there to the  ?

This is right. And then if I apply exponential , to have here E


And then how do you go from there to the  ?

This is right. And then if I apply exponential , to have here E

Look. OK , so let 's  I mean , C equals A plus B ,


This is right. And then if I apply exponential , to have here E

Look. OK , so let 's  I mean , C equals A plus B ,


Look. OK , so let 's  I mean , C equals A plus B ,


S uh , i th we can put here the set transformation.


S uh , i th we can put here the set transformation.

I see. OK , I understand now. Alright , thanks.


I see. OK , I understand now. Alright , thanks.

Yeah. In this case , well , we can put here a  Y.


I see. OK , I understand now. Alright , thanks.

Yeah. In this case , well , we can put here a  Y.

OK. So , yeah. It 's just by definition  that the individual   that the , uh  So , capital X is by definition the same as E to the little X because she 's saying that the little X is  is the , uh  is the log. Alright.


Yeah. In this case , well , we can put here a  Y.

OK. So , yeah. It 's just by definition  that the individual   that the , uh  So , capital X is by definition the same as E to the little X because she 's saying that the little X is  is the , uh  is the log. Alright.


OK. So , yeah. It 's just by definition  that the individual   that the , uh  So , capital X is by definition the same as E to the little X because she 's saying that the little X is  is the , uh  is the log. Alright.


I think these things are a lot clearer when you can use fonts  different fonts there


I think these things are a lot clearer when you can use fonts  different fonts there

so you know which is which. But I  I under I understand what you mean now.


I think these things are a lot clearer when you can use fonts  different fonts there

so you know which is which. But I  I under I understand what you mean now.

Yeah , yeah. That 's true. That 's true.


so you know which is which. But I  I under I understand what you mean now.

Yeah , yeah. That 's true. That 's true.


Yeah , yeah. That 's true. That 's true.


And now I can do it , uh  pfff ! I can put log  of EX  plus log


And now I can do it , uh  pfff ! I can put log  of EX  plus log

Oh. Yes. I understand now. And that 's where it comes from.


Oh. Yes. I understand now. And that 's where it comes from.


Well. The idea  Well , we have fixed this equa


Well. The idea  Well , we have fixed this equa

OK. So now once you get that  that one , then you  then you do a first or second - order , or something , Taylor  series expansion of this.


Well. The idea  Well , we have fixed this equa

OK. So now once you get that  that one , then you  then you do a first or second - order , or something , Taylor  series expansion of this.

Yeah. This is another linear relation that this  to develop this in  vector s Taylor series.


OK. So now once you get that  that one , then you  then you do a first or second - order , or something , Taylor  series expansion of this.

Yeah. This is another linear relation that this  to develop this in  vector s Taylor series.


Yeah. This is another linear relation that this  to develop this in  vector s Taylor series.

Mm - hmm. And for that , well , the goal is to obtain , um    est estimate a PDF for the noisy speech when we have a   a statistic for clean speech and for the noisy speech. Mmm ? And when w the way to obtain the PDF for the noisy speech is  well , we know this statistic and we know the noisy st well , we can apply first order of the vector st Taylor series of the  of the  of  well , the order that we want , increase the complexity of the problem.


Mm - hmm. And for that , well , the goal is to obtain , um    est estimate a PDF for the noisy speech when we have a   a statistic for clean speech and for the noisy speech. Mmm ? And when w the way to obtain the PDF for the noisy speech is  well , we know this statistic and we know the noisy st well , we can apply first order of the vector st Taylor series of the  of the  of  well , the order that we want , increase the complexity of the problem.


Mm - hmm. And for that , well , the goal is to obtain , um    est estimate a PDF for the noisy speech when we have a   a statistic for clean speech and for the noisy speech. Mmm ? And when w the way to obtain the PDF for the noisy speech is  well , we know this statistic and we know the noisy st well , we can apply first order of the vector st Taylor series of the  of the  of  well , the order that we want , increase the complexity of the problem.

And then when we have a expression , uh , for the  mean and variance of the noisy speech , we apply a technique of minimum mean - square estimation


And then when we have a expression , uh , for the  mean and variance of the noisy speech , we apply a technique of minimum mean - square estimation

to obtain the expected value of the clean speech given the  this  statistic for the noisy speech


to obtain the expected value of the clean speech given the  this  statistic for the noisy speech

the statistic for clean speech and the statistic of the noisy speech. This only that. But the idea is that


to obtain the expected value of the clean speech given the  this  statistic for the noisy speech

the statistic for clean speech and the statistic of the noisy speech. This only that. But the idea is that

And the  the model of clean speech is a codebook. Right ?


the statistic for clean speech and the statistic of the noisy speech. This only that. But the idea is that

And the  the model of clean speech is a codebook. Right ?

u Yeah. We have our codebook with different density  Gaussian.


And the  the model of clean speech is a codebook. Right ?

u Yeah. We have our codebook with different density  Gaussian.

We can expre we can put that the  PDF  for the clean test , probability of the clean speech is equal to


u Yeah. We have our codebook with different density  Gaussian.

We can expre we can put that the  PDF  for the clean test , probability of the clean speech is equal to


We can expre we can put that the  PDF  for the clean test , probability of the clean speech is equal to


We can expre we can put that the  PDF  for the clean test , probability of the clean speech is equal to

So , um , how  h how much  in  in the work they reported , how much noisy speech did you need to get , uh , good enough statistics for the  to get this mapping ?


So , um , how  h how much  in  in the work they reported , how much noisy speech did you need to get , uh , good enough statistics for the  to get this mapping ?