That 's on digits. It c it  it could h it could have something to do with , um , downsampling.

That 's  that 's worth looking into. Um , d and , um , ap ap apart from that , I guess the  the main thing I have t ta I have to talk is , um , where I 'm planning to go over the next week. Um. So I 've been working on integrating this mean subtraction approach into the SmartKom system. And there 's this question of , well , so , um , in my tests before with HTK I found it worked  it worked the best with about twelve seconds of data used to estimate the mean , but , we 'll often have less  in the SmartKom system. Um. So I think we 'll use as much data as we have  at a particular time , and we 'll   we 'll concatenate utterances together , um , to get as much data as we possibly can from the user. But ,  um ,  there 's a question of how to set up the models. So um , we could train the models. If we think twelve seconds is ideal we could train the models using twelve seconds to calculate the mean , to mean subtract the training data. Or we could , um , use some other amount. So  like I did an experiment where I , um , was using six seconds in test , um , but , for  I tried twelve seconds in train. And I tried , um , um , the same in train  I 'm a I tried six seconds in train. And six seconds in train  was about point three percent better. Um , and   um , it 's not clear to me yet whether that 's  something significant. So I wanna do some tests and , um ,  actually make some plots of , um  for a particular amount of data and test what happens if you vary the amount of data in train.


That 's  that 's worth looking into. Um , d and , um , ap ap apart from that , I guess the  the main thing I have t ta I have to talk is , um , where I 'm planning to go over the next week. Um. So I 've been working on integrating this mean subtraction approach into the SmartKom system. And there 's this question of , well , so , um , in my tests before with HTK I found it worked  it worked the best with about twelve seconds of data used to estimate the mean , but , we 'll often have less  in the SmartKom system. Um. So I think we 'll use as much data as we have  at a particular time , and we 'll   we 'll concatenate utterances together , um , to get as much data as we possibly can from the user. But ,  um ,  there 's a question of how to set up the models. So um , we could train the models. If we think twelve seconds is ideal we could train the models using twelve seconds to calculate the mean , to mean subtract the training data. Or we could , um , use some other amount. So  like I did an experiment where I , um , was using six seconds in test , um , but , for  I tried twelve seconds in train. And I tried , um , um , the same in train  I 'm a I tried six seconds in train. And six seconds in train  was about point three percent better. Um , and   um , it 's not clear to me yet whether that 's  something significant. So I wanna do some tests and , um ,  actually make some plots of , um  for a particular amount of data and test what happens if you vary the amount of data in train.

Uh , Guenter , I don't know if you t  followed this stuff but this is , uh ,  a uh , uh , long - term  long - term window F F Yeah. Yeah , he  you talked about it.


Uh , Guenter , I don't know if you t  followed this stuff but this is , uh ,  a uh , uh , long - term  long - term window F F Yeah. Yeah , he  you talked about it.


y s so I was  I actually ran the experiments mostly and I  I was  I was hoping to have the plots with me today. I just didn't get to it. But , um  yeah , I wou I would be curious about people 's feedback on this cuz I 'm   @ @  I p I think there are some I think it 's  it 's kind of like a  a bit of a tricky engineering problem. I 'm trying to figure out what 's the optimal way to set this up. So , um ,  I 'll try to make the plots and then put some postscript up on my  on my web page. And I 'll mention it in my status report if people wanna take a look.


y s so I was  I actually ran the experiments mostly and I  I was  I was hoping to have the plots with me today. I just didn't get to it. But , um  yeah , I wou I would be curious about people 's feedback on this cuz I 'm   @ @  I p I think there are some I think it 's  it 's kind of like a  a bit of a tricky engineering problem. I 'm trying to figure out what 's the optimal way to set this up. So , um ,  I 'll try to make the plots and then put some postscript up on my  on my web page. And I 'll mention it in my status report if people wanna take a look.

You could clarify something for me. You 're saying point three percent , you take a point three percent hit ,  when the training and testing links are  don't match or something ?


You could clarify something for me. You 're saying point three percent , you take a point three percent hit ,  when the training and testing links are  don't match or something ?


I  I don't think it  it 's  just for any mismatch  you take a hit.


I  I don't think it  it 's  just for any mismatch  you take a hit.

i In some cases it might be u better to have a mismatch. Like I think I saw something like  like if you only have two seconds in test , or , um , maybe it was something like four seconds , you actually do a little better if you , um ,  train on six seconds than if you train on four seconds.


i In some cases it might be u better to have a mismatch. Like I think I saw something like  like if you only have two seconds in test , or , um , maybe it was something like four seconds , you actually do a little better if you , um ,  train on six seconds than if you train on four seconds.

Um , but the case , uh  with the point three percent hit was  using six seconds in test , um , comparing train on twelve seconds  versus train on six seconds.


Um , but the case , uh  with the point three percent hit was  using six seconds in test , um , comparing train on twelve seconds  versus train on six seconds.


Um , but the case , uh  with the point three percent hit was  using six seconds in test , um , comparing train on twelve seconds  versus train on six seconds.

OK. But point three percent , uh , w from what to what ? That 's point three percent


OK. But point three percent , uh , w from what to what ? That 's point three percent

On  The  the  the accuracies  w went from  it was something vaguely like ninety - five point six accuracy , um , improved to ninety - five point nine wh when I


On  The  the  the accuracies  w went from  it was something vaguely like ninety - five point six accuracy , um , improved to ninety - five point nine wh when I

So  yeah. So about a  about an eight percent , uh , seven or eight percent relative ?


So  yeah. So about a  about an eight percent , uh , seven or eight percent relative ?


So  yeah. So about a  about an eight percent , uh , seven or eight percent relative ?

Uh , Yeah. Well , I think in a p You know , if  if you were going for an evaluation system you 'd care. But if you were doing a live system that people were actually using nobody would notice. It 's  uh , I think the thing is to get something that 's practical , that  that you could really use.


So  yeah. So about a  about an eight percent , uh , seven or eight percent relative ?

Uh , Yeah. Well , I think in a p You know , if  if you were going for an evaluation system you 'd care. But if you were doing a live system that people were actually using nobody would notice. It 's  uh , I think the thing is to get something that 's practical , that  that you could really use.

Huh. That 's  that 's interesting. Alright , the e uh , I see your point. I guess I was thinking of it as , um ,  an interesting research problem. The  how to g I was thinking that for the ASRU paper we could have a section saying ,  " For SmartKom , we  we d in  we tried this approach in , uh ,  interactive system " , which I don't think has been done before.


Uh , Yeah. Well , I think in a p You know , if  if you were going for an evaluation system you 'd care. But if you were doing a live system that people were actually using nobody would notice. It 's  uh , I think the thing is to get something that 's practical , that  that you could really use.

Huh. That 's  that 's interesting. Alright , the e uh , I see your point. I guess I was thinking of it as , um ,  an interesting research problem. The  how to g I was thinking that for the ASRU paper we could have a section saying ,  " For SmartKom , we  we d in  we tried this approach in , uh ,  interactive system " , which I don't think has been done before.


Uh , Yeah. Well , I think in a p You know , if  if you were going for an evaluation system you 'd care. But if you were doing a live system that people were actually using nobody would notice. It 's  uh , I think the thing is to get something that 's practical , that  that you could really use.

Huh. That 's  that 's interesting. Alright , the e uh , I see your point. I guess I was thinking of it as , um ,  an interesting research problem. The  how to g I was thinking that for the ASRU paper we could have a section saying ,  " For SmartKom , we  we d in  we tried this approach in , uh ,  interactive system " , which I don't think has been done before.

And  and then there was two research questions from that.


Huh. That 's  that 's interesting. Alright , the e uh , I see your point. I guess I was thinking of it as , um ,  an interesting research problem. The  how to g I was thinking that for the ASRU paper we could have a section saying ,  " For SmartKom , we  we d in  we tried this approach in , uh ,  interactive system " , which I don't think has been done before.

And  and then there was two research questions from that.


And  and then there was two research questions from that.

And one is the k does it still work if you just use the past history ?


And one is the k does it still work if you just use the past history ?

Alright , and the other was this question of , um what I was just talking about now. So I guess that 's why I thought it was interesting.


And one is the k does it still work if you just use the past history ?

Alright , and the other was this question of , um what I was just talking about now. So I guess that 's why I thought it was interesting.

I mean , a short - time FFT  short - time cepstrum calculation , uh , mean  u mean calculation work that people have in commercial systems , they do this all the time. They  the  they calculate it from previous utterances and then use it , you know.


Alright , and the other was this question of , um what I was just talking about now. So I guess that 's why I thought it was interesting.

I mean , a short - time FFT  short - time cepstrum calculation , uh , mean  u mean calculation work that people have in commercial systems , they do this all the time. They  the  they calculate it from previous utterances and then use it , you know.


Alright , and the other was this question of , um what I was just talking about now. So I guess that 's why I thought it was interesting.

I mean , a short - time FFT  short - time cepstrum calculation , uh , mean  u mean calculation work that people have in commercial systems , they do this all the time. They  the  they calculate it from previous utterances and then use it , you know.

But  but , uh , as you say , there hasn't been that much with this long  long - time , uh , spectra work.


I mean , a short - time FFT  short - time cepstrum calculation , uh , mean  u mean calculation work that people have in commercial systems , they do this all the time. They  the  they calculate it from previous utterances and then use it , you know.

But  but , uh , as you say , there hasn't been that much with this long  long - time , uh , spectra work.


But  but , uh , as you say , there hasn't been that much with this long  long - time , uh , spectra work.


But  but , uh , as you say , there hasn't been that much with this long  long - time , uh , spectra work.

So that 's  that 's  that 's standard. Um


So that 's  that 's  that 's standard. Um


So that 's  that 's  that 's standard. Um

Yeah. Um , but , u uh , yes. No , it is interesting. And the other thing is , I mean , there 's two sides to these really small , uh , gradations in performance. Um , I mean , on the one hand in a practical system if something is , uh , four point four percent error , four point one percent error , people won't really tell  be able to tell the difference. On the other hand , when you 're doing , uh , research , you may , eh  you might find that the way that you build up a change from a ninety - five percent accurate system to a ninety - eight percent accurate system is through ten or twelve little things that you do that each are point three percent. So  so the  they  they  it 's  I don't mean to say that they 're  they 're irrelevant. Uh , they are relevant. But , um ,  i for a demo , you won't see it.


Yeah. Um , but , u uh , yes. No , it is interesting. And the other thing is , I mean , there 's two sides to these really small , uh , gradations in performance. Um , I mean , on the one hand in a practical system if something is , uh , four point four percent error , four point one percent error , people won't really tell  be able to tell the difference. On the other hand , when you 're doing , uh , research , you may , eh  you might find that the way that you build up a change from a ninety - five percent accurate system to a ninety - eight percent accurate system is through ten or twelve little things that you do that each are point three percent. So  so the  they  they  it 's  I don't mean to say that they 're  they 're irrelevant. Uh , they are relevant. But , um ,  i for a demo , you won't see it.


Yeah. Um , but , u uh , yes. No , it is interesting. And the other thing is , I mean , there 's two sides to these really small , uh , gradations in performance. Um , I mean , on the one hand in a practical system if something is , uh , four point four percent error , four point one percent error , people won't really tell  be able to tell the difference. On the other hand , when you 're doing , uh , research , you may , eh  you might find that the way that you build up a change from a ninety - five percent accurate system to a ninety - eight percent accurate system is through ten or twelve little things that you do that each are point three percent. So  so the  they  they  it 's  I don't mean to say that they 're  they 're irrelevant. Uh , they are relevant. But , um ,  i for a demo , you won't see it.

And , um , Let 's  l let 's see. Um , OK. And then there 's um , another thing I wanna start looking at , um ,  wi is , um , the choice of the analysis window length. So I 've just been using two seconds just because that 's what Carlos did before. Uh , I wrote to him asking about he chose the two seconds. And it seemed like he chose it a bit informally. So , um , with the  with the HTK set - up I should be able to do some experiments , on just varying that length , say between one and three seconds , in a few different reverberation conditions , um , say this room and also a few of the artificial impulse responses we have for reverberation , just , um , making some plots and seeing how they look. And , um , so , with the  the sampling rate I was using , one second or two seconds or four seconds is at a power of two um , number of samples and , um , I 'll  I 'll jus f for the ones in between I guess I 'll just zero - pad.


And , um , Let 's  l let 's see. Um , OK. And then there 's um , another thing I wanna start looking at , um ,  wi is , um , the choice of the analysis window length. So I 've just been using two seconds just because that 's what Carlos did before. Uh , I wrote to him asking about he chose the two seconds. And it seemed like he chose it a bit informally. So , um , with the  with the HTK set - up I should be able to do some experiments , on just varying that length , say between one and three seconds , in a few different reverberation conditions , um , say this room and also a few of the artificial impulse responses we have for reverberation , just , um , making some plots and seeing how they look. And , um , so , with the  the sampling rate I was using , one second or two seconds or four seconds is at a power of two um , number of samples and , um , I 'll  I 'll jus f for the ones in between I guess I 'll just zero - pad.

Mm - hmm. I guess one thing that might also be an issue , uh , cuz part of what you 're doing is you 're getting a  a spectrum over a bunch of different kinds of speech sounds. Um , and so it might matter how fast someone was talking for instance.


And , um , Let 's  l let 's see. Um , OK. And then there 's um , another thing I wanna start looking at , um ,  wi is , um , the choice of the analysis window length. So I 've just been using two seconds just because that 's what Carlos did before. Uh , I wrote to him asking about he chose the two seconds. And it seemed like he chose it a bit informally. So , um , with the  with the HTK set - up I should be able to do some experiments , on just varying that length , say between one and three seconds , in a few different reverberation conditions , um , say this room and also a few of the artificial impulse responses we have for reverberation , just , um , making some plots and seeing how they look. And , um , so , with the  the sampling rate I was using , one second or two seconds or four seconds is at a power of two um , number of samples and , um , I 'll  I 'll jus f for the ones in between I guess I 'll just zero - pad.

Mm - hmm. I guess one thing that might also be an issue , uh , cuz part of what you 're doing is you 're getting a  a spectrum over a bunch of different kinds of speech sounds. Um , and so it might matter how fast someone was talking for instance.

You know , if you  if  if  if there 's a lot of phones in one second maybe you 'll get a  a really good sampling of all these different things , and   and , uh , on the other hand if someone 's talking slowly maybe you 'd need more. So


Mm - hmm. I guess one thing that might also be an issue , uh , cuz part of what you 're doing is you 're getting a  a spectrum over a bunch of different kinds of speech sounds. Um , and so it might matter how fast someone was talking for instance.

You know , if you  if  if  if there 's a lot of phones in one second maybe you 'll get a  a really good sampling of all these different things , and   and , uh , on the other hand if someone 's talking slowly maybe you 'd need more. So


You know , if you  if  if  if there 's a lot of phones in one second maybe you 'll get a  a really good sampling of all these different things , and   and , uh , on the other hand if someone 's talking slowly maybe you 'd need more. So

I don't know if you have some samples of faster or slower speech but it might make a difference. I don't know.


You know , if you  if  if  if there 's a lot of phones in one second maybe you 'll get a  a really good sampling of all these different things , and   and , uh , on the other hand if someone 's talking slowly maybe you 'd need more. So

I don't know if you have some samples of faster or slower speech but it might make a difference. I don't know.

Uh , yeah , I don't  I don't think the TI - digits data that I have , um ,  i is  would be appropriate for that.


I don't know if you have some samples of faster or slower speech but it might make a difference. I don't know.

Uh , yeah , I don't  I don't think the TI - digits data that I have , um ,  i is  would be appropriate for that.


I don't know if you have some samples of faster or slower speech but it might make a difference. I don't know.

Uh , yeah , I don't  I don't think the TI - digits data that I have , um ,  i is  would be appropriate for that.

But what do you  What about if I w I fed it through some kind of , um , speech processing algorithm that changed the speech rate ?


Uh , yeah , I don't  I don't think the TI - digits data that I have , um ,  i is  would be appropriate for that.

But what do you  What about if I w I fed it through some kind of , um , speech processing algorithm that changed the speech rate ?

Yeah , but then you 'll have the degradation of  of , uh , whatever you do uh , added onto that. But maybe. Yeah , maybe if you get something that sounds  that  that 's  does a pretty job at that.


But what do you  What about if I w I fed it through some kind of , um , speech processing algorithm that changed the speech rate ?

Yeah , but then you 'll have the degradation of  of , uh , whatever you do uh , added onto that. But maybe. Yeah , maybe if you get something that sounds  that  that 's  does a pretty job at that.

Yeah. Well , uh , just if you think it 's worth looking into.


Yeah , but then you 'll have the degradation of  of , uh , whatever you do uh , added onto that. But maybe. Yeah , maybe if you get something that sounds  that  that 's  does a pretty job at that.

Yeah. Well , uh , just if you think it 's worth looking into.

I mean , it  it is getting a little away from reverberation.


Yeah. Well , uh , just if you think it 's worth looking into.

I mean , it  it is getting a little away from reverberation.

Um , yeah. It 's just that you 're making a choice  uh , I was thinking more from the system aspect , if you 're making a choice for SmartKom , that  that  that it might be that it 's  it c the optimal number could be different , depending on


I mean , it  it is getting a little away from reverberation.

Um , yeah. It 's just that you 're making a choice  uh , I was thinking more from the system aspect , if you 're making a choice for SmartKom , that  that  that it might be that it 's  it c the optimal number could be different , depending on


Um , yeah. It 's just that you 're making a choice  uh , I was thinking more from the system aspect , if you 're making a choice for SmartKom , that  that  that it might be that it 's  it c the optimal number could be different , depending on

And  and th the third thing , um , uh , is , um , Barry explained LDA filtering to me yesterday. And so , um , Mike Shire in his thesis um ,  did a  a series of experiments , um , training LDA filters in d on different conditions. And you were interested in having me repeat this for  for this mean subtraction approach ? Is  is that right ? Or for these long analysis windows , I guess , is the right way to put it.


And  and th the third thing , um , uh , is , um , Barry explained LDA filtering to me yesterday. And so , um , Mike Shire in his thesis um ,  did a  a series of experiments , um , training LDA filters in d on different conditions. And you were interested in having me repeat this for  for this mean subtraction approach ? Is  is that right ? Or for these long analysis windows , I guess , is the right way to put it.

I guess , the  the  the issue I was  the general issue I was bringing up was that if you 're  have a moving   moving window , uh , a wa a  a set of weights times things that , uh , move along , shift along in time , that you have in fact a linear time invariant filter. And you just happened to have picked a particular one by setting all the weights to be equal. And so the issue is what are some other filters that you could use , uh , in that sense of " filter " ?


And  and th the third thing , um , uh , is , um , Barry explained LDA filtering to me yesterday. And so , um , Mike Shire in his thesis um ,  did a  a series of experiments , um , training LDA filters in d on different conditions. And you were interested in having me repeat this for  for this mean subtraction approach ? Is  is that right ? Or for these long analysis windows , I guess , is the right way to put it.

I guess , the  the  the issue I was  the general issue I was bringing up was that if you 're  have a moving   moving window , uh , a wa a  a set of weights times things that , uh , move along , shift along in time , that you have in fact a linear time invariant filter. And you just happened to have picked a particular one by setting all the weights to be equal. And so the issue is what are some other filters that you could use , uh , in that sense of " filter " ?

And , um , as I was saying , I think the simplest thing to do is not to train anything , but just to do some sort of , uh , uh , hamming or Hanning , uh , kind of window , kind of thing ,


I guess , the  the  the issue I was  the general issue I was bringing up was that if you 're  have a moving   moving window , uh , a wa a  a set of weights times things that , uh , move along , shift along in time , that you have in fact a linear time invariant filter. And you just happened to have picked a particular one by setting all the weights to be equal. And so the issue is what are some other filters that you could use , uh , in that sense of " filter " ?

And , um , as I was saying , I think the simplest thing to do is not to train anything , but just to do some sort of , uh , uh , hamming or Hanning , uh , kind of window , kind of thing ,


And , um , as I was saying , I think the simplest thing to do is not to train anything , but just to do some sort of , uh , uh , hamming or Hanning , uh , kind of window , kind of thing ,

just sort of to de - emphasize the jarring. So I think that would sort of be the first thing to do. But then , yeah , the LDA i uh , is interesting because it would sort of say well , suppose you actually trained this up to do the best you could by some criterion , what would the filter look like then ?


just sort of to de - emphasize the jarring. So I think that would sort of be the first thing to do. But then , yeah , the LDA i uh , is interesting because it would sort of say well , suppose you actually trained this up to do the best you could by some criterion , what would the filter look like then ?

Uh , and , um , that 's sort of what we 're doing in this Aur - Aurora stuff. And , uh , it 's still not clear to me in the long run whether the best thing to do would be to do that or to have some stylized version of the filter that looks like these things you 've trained up , because you always have the problem that it 's trained up for one condition and it isn't quite right for another. So. uh  that 's  that 's why  that 's why RASTA filter has actually ended up lasting a long time , people still using it quite a bit , because y you don't change it. So doesn't get any worse. Uh ,


Uh , and , um , that 's sort of what we 're doing in this Aur - Aurora stuff. And , uh , it 's still not clear to me in the long run whether the best thing to do would be to do that or to have some stylized version of the filter that looks like these things you 've trained up , because you always have the problem that it 's trained up for one condition and it isn't quite right for another. So. uh  that 's  that 's why  that 's why RASTA filter has actually ended up lasting a long time , people still using it quite a bit , because y you don't change it. So doesn't get any worse. Uh ,


Uh , and , um , that 's sort of what we 're doing in this Aur - Aurora stuff. And , uh , it 's still not clear to me in the long run whether the best thing to do would be to do that or to have some stylized version of the filter that looks like these things you 've trained up , because you always have the problem that it 's trained up for one condition and it isn't quite right for another. So. uh  that 's  that 's why  that 's why RASTA filter has actually ended up lasting a long time , people still using it quite a bit , because y you don't change it. So doesn't get any worse. Uh ,

o OK. So , um , a actually I was just thinking about what I was asking about earlier , wi which is about having  less than say twelve seconds in the SmartKom system to do the mean subtraction. You said in  systems where you use cepstral mean subtraction , they concatenate utterances and ,  do you know how they address this issue of , um , testing versus training ? Can


o OK. So , um , a actually I was just thinking about what I was asking about earlier , wi which is about having  less than say twelve seconds in the SmartKom system to do the mean subtraction. You said in  systems where you use cepstral mean subtraction , they concatenate utterances and ,  do you know how they address this issue of , um , testing versus training ? Can


o OK. So , um , a actually I was just thinking about what I was asking about earlier , wi which is about having  less than say twelve seconds in the SmartKom system to do the mean subtraction. You said in  systems where you use cepstral mean subtraction , they concatenate utterances and ,  do you know how they address this issue of , um , testing versus training ? Can

I think what they do is they do it always on - line , I mean , that you just take what you have from the past , that you calculate the mean of this and subtract the mean.


I think what they do is they do it always on - line , I mean , that you just take what you have from the past , that you calculate the mean of this and subtract the mean.

And then you can  yeah , you  you can increase your window whi while you get  while you are getting more samples.


I think what they do is they do it always on - line , I mean , that you just take what you have from the past , that you calculate the mean of this and subtract the mean.

And then you can  yeah , you  you can increase your window whi while you get  while you are getting more samples.

OK , um , and , um , so  so in tha in that case , wh what do they do when they 're t um , performing the cepstral mean subtraction on the training data ? So  because you 'd have hours and hours of training data. So do they cut it off and start over ? At intervals ? Or  ?


And then you can  yeah , you  you can increase your window whi while you get  while you are getting more samples.

OK , um , and , um , so  so in tha in that case , wh what do they do when they 're t um , performing the cepstral mean subtraction on the training data ? So  because you 'd have hours and hours of training data. So do they cut it off and start over ? At intervals ? Or  ?

So do you have  uh , you  you mean you have files which are hours of hours long ? Or  ?


OK , um , and , um , so  so in tha in that case , wh what do they do when they 're t um , performing the cepstral mean subtraction on the training data ? So  because you 'd have hours and hours of training data. So do they cut it off and start over ? At intervals ? Or  ?

So do you have  uh , you  you mean you have files which are hours of hours long ? Or  ?

Yeah. I mean , usually you have in the training set you have similar conditions , I mean , file lengths are , I guess the same order or in the same size as for test data , or aren't they ?


So do you have  uh , you  you mean you have files which are hours of hours long ? Or  ?

Yeah. I mean , usually you have in the training set you have similar conditions , I mean , file lengths are , I guess the same order or in the same size as for test data , or aren't they ?

OK. But it 's  OK. So if someone 's interacting with the system , though , uh , Morgan  uh , Morgan said that you would  tend to , um ,  chain utterances together um , r


Yeah. I mean , usually you have in the training set you have similar conditions , I mean , file lengths are , I guess the same order or in the same size as for test data , or aren't they ?

OK. But it 's  OK. So if someone 's interacting with the system , though , uh , Morgan  uh , Morgan said that you would  tend to , um ,  chain utterances together um , r

Well , I think what I was s I thought what I was saying was that , um , at any given point you are gonna start off with what you had from before.


OK. But it 's  OK. So if someone 's interacting with the system , though , uh , Morgan  uh , Morgan said that you would  tend to , um ,  chain utterances together um , r

Well , I think what I was s I thought what I was saying was that , um , at any given point you are gonna start off with what you had from before.

From  and so if you 're splitting things up into utterances  So , for instance , in a dialogue system ,  where you 're gonna be asking , uh , you know , th for some information , there 's some initial th something. And , you know , the first time out you  you might have some general average. But you  you d you don't have very much information yet. But at  after they 've given one utterance you 've got something. You can compute your mean cepstra from that ,


Well , I think what I was s I thought what I was saying was that , um , at any given point you are gonna start off with what you had from before.

From  and so if you 're splitting things up into utterances  So , for instance , in a dialogue system ,  where you 're gonna be asking , uh , you know , th for some information , there 's some initial th something. And , you know , the first time out you  you might have some general average. But you  you d you don't have very much information yet. But at  after they 've given one utterance you 've got something. You can compute your mean cepstra from that ,


From  and so if you 're splitting things up into utterances  So , for instance , in a dialogue system ,  where you 're gonna be asking , uh , you know , th for some information , there 's some initial th something. And , you know , the first time out you  you might have some general average. But you  you d you don't have very much information yet. But at  after they 've given one utterance you 've got something. You can compute your mean cepstra from that ,

and then can use it for the next thing that they say , uh , so that , you know , the performance should be better that second time. Um ,  and I think the heuristics of exactly how people handle that and how they handle their training I 'm sure vary from place to place. But I think the  ideally , it seems to me anyway , that you  you would wanna do the same thing in training as you do in test. But that 's  that 's just , uh , a prejudice. And I think anybody working on this with some particular task would experiment.


From  and so if you 're splitting things up into utterances  So , for instance , in a dialogue system ,  where you 're gonna be asking , uh , you know , th for some information , there 's some initial th something. And , you know , the first time out you  you might have some general average. But you  you d you don't have very much information yet. But at  after they 've given one utterance you 've got something. You can compute your mean cepstra from that ,

and then can use it for the next thing that they say , uh , so that , you know , the performance should be better that second time. Um ,  and I think the heuristics of exactly how people handle that and how they handle their training I 'm sure vary from place to place. But I think the  ideally , it seems to me anyway , that you  you would wanna do the same thing in training as you do in test. But that 's  that 's just , uh , a prejudice. And I think anybody working on this with some particular task would experiment.

Right. I g I guess the question I had was , um , amount of data e u was the amount of data that you 'd give it to , um  update this estimate. Because say you  if you have say five thousand utterances in your training set ,  um , and you  you keep the mean from the last utterance , by the time it gets to the five thousandth utterance


and then can use it for the next thing that they say , uh , so that , you know , the performance should be better that second time. Um ,  and I think the heuristics of exactly how people handle that and how they handle their training I 'm sure vary from place to place. But I think the  ideally , it seems to me anyway , that you  you would wanna do the same thing in training as you do in test. But that 's  that 's just , uh , a prejudice. And I think anybody working on this with some particular task would experiment.

Right. I g I guess the question I had was , um , amount of data e u was the amount of data that you 'd give it to , um  update this estimate. Because say you  if you have say five thousand utterances in your training set ,  um , and you  you keep the mean from the last utterance , by the time it gets to the five thousandth utterance

No , but those are all different people with different  I mean , i in y So for instance , in  in the  in a telephone task , these are different phone calls. So you don't wanna @ @  chain it together from a  from a different phone call.


Right. I g I guess the question I had was , um , amount of data e u was the amount of data that you 'd give it to , um  update this estimate. Because say you  if you have say five thousand utterances in your training set ,  um , and you  you keep the mean from the last utterance , by the time it gets to the five thousandth utterance

No , but those are all different people with different  I mean , i in y So for instance , in  in the  in a telephone task , these are different phone calls. So you don't wanna @ @  chain it together from a  from a different phone call.

So it 's within speaker , within phone call ,


No , but those are all different people with different  I mean , i in y So for instance , in  in the  in a telephone task , these are different phone calls. So you don't wanna @ @  chain it together from a  from a different phone call.

So it 's within speaker , within phone call ,


So it 's within speaker , within phone call ,

if it 's a dialogue system , it 's within whatever this characteristic you 're trying to get rid of is expected to be consistent over ,


if it 's a dialogue system , it 's within whatever this characteristic you 're trying to get rid of is expected to be consistent over ,


right. OK , so you 'd  you  and so in training you would start over at  at every new phone call or at every  new speaker. Yeah ,


right. OK , so you 'd  you  and so in training you would start over at  at every new phone call or at every  new speaker. Yeah ,

Yeah. Now ,  you know , maybe you 'd use something from the others just because at the beginning of a call you don't know anything , and so you might have some kind of general thing that 's your best guess to start with. But  So , s I  I  you know , a lot of these things are proprietary so we 're doing a little bit of guesswork here. I mean , what do comp what do people do who really face these problems in the field ? Well , they have companies and they don't tell other people exactly what they do.


Yeah. Now ,  you know , maybe you 'd use something from the others just because at the beginning of a call you don't know anything , and so you might have some kind of general thing that 's your best guess to start with. But  So , s I  I  you know , a lot of these things are proprietary so we 're doing a little bit of guesswork here. I mean , what do comp what do people do who really face these problems in the field ? Well , they have companies and they don't tell other people exactly what they do.


Yeah. Now ,  you know , maybe you 'd use something from the others just because at the beginning of a call you don't know anything , and so you might have some kind of general thing that 's your best guess to start with. But  So , s I  I  you know , a lot of these things are proprietary so we 're doing a little bit of guesswork here. I mean , what do comp what do people do who really face these problems in the field ? Well , they have companies and they don't tell other people exactly what they do.

But  but I mean , when you  the  the hints that you get from what they  when they talk about it are that they do  they all do something like this.


Yeah. Now ,  you know , maybe you 'd use something from the others just because at the beginning of a call you don't know anything , and so you might have some kind of general thing that 's your best guess to start with. But  So , s I  I  you know , a lot of these things are proprietary so we 're doing a little bit of guesswork here. I mean , what do comp what do people do who really face these problems in the field ? Well , they have companies and they don't tell other people exactly what they do.

But  but I mean , when you  the  the hints that you get from what they  when they talk about it are that they do  they all do something like this.

Right , OK. I see. Bec - because I  so this SmartKom task first off , it 's this TV and movie information system.


But  but I mean , when you  the  the hints that you get from what they  when they talk about it are that they do  they all do something like this.

Right , OK. I see. Bec - because I  so this SmartKom task first off , it 's this TV and movie information system.

Yeah , but you might have somebody who 's using it


Right , OK. I see. Bec - because I  so this SmartKom task first off , it 's this TV and movie information system.

Yeah , but you might have somebody who 's using it

and then later you might have somebody else who 's using it.


Yeah , but you might have somebody who 's using it

and then later you might have somebody else who 's using it.


and then later you might have somebody else who 's using it.


and then later you might have somebody else who 's using it.

I was  I was about to say. So if  if you ask it " What  what movies are on TV tonight ? " ,


I was  I was about to say. So if  if you ask it " What  what movies are on TV tonight ? " ,


I was  I was about to say. So if  if you ask it " What  what movies are on TV tonight ? " ,

if I look at my wristwatch when I say that it 's about two seconds. The way I currently have the mean subtraction , um , set up , the  the analysis window is two seconds.


if I look at my wristwatch when I say that it 's about two seconds. The way I currently have the mean subtraction , um , set up , the  the analysis window is two seconds.

So what you just said , about what do you start with , raises a question of  what do I start with then ?


So what you just said , about what do you start with , raises a question of  what do I start with then ?


So what you just said , about what do you start with , raises a question of  what do I start with then ?

Well , w OK , so in that situation , though , th maybe what 's a little different there , is I think you 're talking about  there 's only one  it  it  it also depends  we 're getting a little off track here.


Well , w OK , so in that situation , though , th maybe what 's a little different there , is I think you 're talking about  there 's only one  it  it  it also depends  we 're getting a little off track here.


Well , w OK , so in that situation , though , th maybe what 's a little different there , is I think you 're talking about  there 's only one  it  it  it also depends  we 're getting a little off track here.

r But  but  but  Uh , there 's been some discussion about whether the work we 're doing in that project is gonna be for the kiosk or for the mobile or for both. And I think for this kind of discussion it matters. If it 's in the kiosk , then the physical situation is the same. It 's gonna  you know , the exact interaction of the microphone 's gonna differ depending on the person and so forth. But at least the basic acoustics are gonna be the same. So f if it 's really in one kiosk , then I think that you could just chain together and  and you know , as much  as much speech as possible to  because what you 're really trying to get at is the  is the reverberation characteristic.


r But  but  but  Uh , there 's been some discussion about whether the work we 're doing in that project is gonna be for the kiosk or for the mobile or for both. And I think for this kind of discussion it matters. If it 's in the kiosk , then the physical situation is the same. It 's gonna  you know , the exact interaction of the microphone 's gonna differ depending on the person and so forth. But at least the basic acoustics are gonna be the same. So f if it 's really in one kiosk , then I think that you could just chain together and  and you know , as much  as much speech as possible to  because what you 're really trying to get at is the  is the reverberation characteristic.

But in  in the case of the mobile , uh ,  presumably the acoustic 's changing all over the place.


But in  in the case of the mobile , uh ,  presumably the acoustic 's changing all over the place.

And in that case you probably don't wanna have it be endless because you wanna have some sort of  it 's  it 's not a question of how long do you think it 's  you can get an approximation to a stationary something , given that it 's not really stationary.


And in that case you probably don't wanna have it be endless because you wanna have some sort of  it 's  it 's not a question of how long do you think it 's  you can get an approximation to a stationary something , given that it 's not really stationary.


And I  I g I guess I s just started thinking of another question , which is ,  for  for the very first frame , w what  what do I do if I 'm  if I take  if I use that frame to calculate the mean , then I 'm just gonna get n nothing.


so I should probably have some kind of default  mean for the first f couple of frames ?


so I should probably have some kind of default  mean for the first f couple of frames ?

Yeah. Or subtract nothing. I mean , it 's


Yeah. Or subtract nothing. I mean , it 's

Or subtract nothing. And  and that 's  that 's  I guess that 's something that 's p people have figured out how to deal with in cepstral mean subtraction as well ?


Yeah. Or subtract nothing. I mean , it 's

Or subtract nothing. And  and that 's  that 's  I guess that 's something that 's p people have figured out how to deal with in cepstral mean subtraction as well ?

Yeah , yeah. Yeah , people do something. They  they , uh , they have some , um , uh , in  in cepstral mean subtraction , for short - term window  analysis windows , as is usually done , you 're trying to get rid of some very general characteristic. And so , uh , if you have any other information about what a general kind of characteristic would be , then you  you can do it there.


Yeah. Or subtract nothing. I mean , it 's

Or subtract nothing. And  and that 's  that 's  I guess that 's something that 's p people have figured out how to deal with in cepstral mean subtraction as well ?

Yeah , yeah. Yeah , people do something. They  they , uh , they have some , um , uh , in  in cepstral mean subtraction , for short - term window  analysis windows , as is usually done , you 're trying to get rid of some very general characteristic. And so , uh , if you have any other information about what a general kind of characteristic would be , then you  you can do it there.

You can also  you can also reflect the data. So you take , uh  you know , I 'm not sure how many frames you need.


Or subtract nothing. And  and that 's  that 's  I guess that 's something that 's p people have figured out how to deal with in cepstral mean subtraction as well ?

Yeah , yeah. Yeah , people do something. They  they , uh , they have some , um , uh , in  in cepstral mean subtraction , for short - term window  analysis windows , as is usually done , you 're trying to get rid of some very general characteristic. And so , uh , if you have any other information about what a general kind of characteristic would be , then you  you can do it there.

You can also  you can also reflect the data. So you take , uh  you know , I 'm not sure how many frames you need.


Yeah , yeah. Yeah , people do something. They  they , uh , they have some , um , uh , in  in cepstral mean subtraction , for short - term window  analysis windows , as is usually done , you 're trying to get rid of some very general characteristic. And so , uh , if you have any other information about what a general kind of characteristic would be , then you  you can do it there.

You can also  you can also reflect the data. So you take , uh  you know , I 'm not sure how many frames you need.

But you take that many from the front and flip it around to  a as the negative value.


You can also  you can also reflect the data. So you take , uh  you know , I 'm not sure how many frames you need.

But you take that many from the front and flip it around to  a as the negative value.


But you take that many from the front and flip it around to  a as the negative value.


But you take that many from the front and flip it around to  a as the negative value.

The other thing is that  and  and  I  I remember B B N doing this , is that if you have a multi - pass system , um , if the first pass ta it takes most of the computation , the second and the third pass could be very , very quick ,


The other thing is that  and  and  I  I remember B B N doing this , is that if you have a multi - pass system , um , if the first pass ta it takes most of the computation , the second and the third pass could be very , very quick ,


The other thing is that  and  and  I  I remember B B N doing this , is that if you have a multi - pass system , um , if the first pass ta it takes most of the computation , the second and the third pass could be very , very quick ,

just looking at a relatively small n small , uh , space of hypotheses.


just looking at a relatively small n small , uh , space of hypotheses.

Then you can do your first pass  without any subtraction at all.


Then you can do your first pass  without any subtraction at all.

And then your second pass , uh , uh , eliminates those  most of those hypotheses by , uh  by having an improved  improved version o of the analysis.


And then your second pass , uh , uh , eliminates those  most of those hypotheses by , uh  by having an improved  improved version o of the analysis.


Yeah , OK. Um , so for the past ,  uh , week an or two , I 've been just writing my , uh , formal thesis proposal. Um , so I 'm taking  this qualifier exam that 's coming up in two weeks. And I  I finish writing a proposal and submit it to the committee. Um. And uh , should I  should I explain , uh , more about what  what I 'm proposing to do , and s and stuff ?


Yeah , OK. Um , so for the past ,  uh , week an or two , I 've been just writing my , uh , formal thesis proposal. Um , so I 'm taking  this qualifier exam that 's coming up in two weeks. And I  I finish writing a proposal and submit it to the committee. Um. And uh , should I  should I explain , uh , more about what  what I 'm proposing to do , and s and stuff ?

OK. Um , so briefly ,  I 'm proposing to do a n a new p approach to speech recognition using um , a combination of , uh , multi - band ideas and ideas , um ,    about the uh , acoustic phonec phonetic approach to speech recognition. Um , so I will be using  these graphical models that  um , that implement the multi - band approach  to recognize a set of intermediate categories that might involve , uh , things like phonetic features  or other  other f feature things that are more closely related to the acoustic signal itself. Um , and the hope in all of this is that by going multi - band and by going into these ,  um intermediate classifications ,  that we can get a system that 's more robust to  to unseen noises , and situations like that. Um , and so , some of the research issues involved in this are ,  um ,   one , what kind of intermediate categories do we need to classify ? Um , another one is  um , what  what other types of structures in these multi - band graphical models should we consider in order to um , combine evidence from  the sub - bands ? And , uh , the third one is how do we  how do we merge all the , uh , information from the individual uh , multi - band classifiers to come up with word  word recognition or  or phone recognition things. Um , so basically that 's  that 's what I 've been doing. And ,


OK. Um , so briefly ,  I 'm proposing to do a n a new p approach to speech recognition using um , a combination of , uh , multi - band ideas and ideas , um ,    about the uh , acoustic phonec phonetic approach to speech recognition. Um , so I will be using  these graphical models that  um , that implement the multi - band approach  to recognize a set of intermediate categories that might involve , uh , things like phonetic features  or other  other f feature things that are more closely related to the acoustic signal itself. Um , and the hope in all of this is that by going multi - band and by going into these ,  um intermediate classifications ,  that we can get a system that 's more robust to  to unseen noises , and situations like that. Um , and so , some of the research issues involved in this are ,  um ,   one , what kind of intermediate categories do we need to classify ? Um , another one is  um , what  what other types of structures in these multi - band graphical models should we consider in order to um , combine evidence from  the sub - bands ? And , uh , the third one is how do we  how do we merge all the , uh , information from the individual uh , multi - band classifiers to come up with word  word recognition or  or phone recognition things. Um , so basically that 's  that 's what I 've been doing. And ,


OK. Um , so briefly ,  I 'm proposing to do a n a new p approach to speech recognition using um , a combination of , uh , multi - band ideas and ideas , um ,    about the uh , acoustic phonec phonetic approach to speech recognition. Um , so I will be using  these graphical models that  um , that implement the multi - band approach  to recognize a set of intermediate categories that might involve , uh , things like phonetic features  or other  other f feature things that are more closely related to the acoustic signal itself. Um , and the hope in all of this is that by going multi - band and by going into these ,  um intermediate classifications ,  that we can get a system that 's more robust to  to unseen noises , and situations like that. Um , and so , some of the research issues involved in this are ,  um ,   one , what kind of intermediate categories do we need to classify ? Um , another one is  um , what  what other types of structures in these multi - band graphical models should we consider in order to um , combine evidence from  the sub - bands ? And , uh , the third one is how do we  how do we merge all the , uh , information from the individual uh , multi - band classifiers to come up with word  word recognition or  or phone recognition things. Um , so basically that 's  that 's what I 've been doing. And ,

I got two weeks to brush up on d um , presentation stuff and , um ,


OK. Um , so briefly ,  I 'm proposing to do a n a new p approach to speech recognition using um , a combination of , uh , multi - band ideas and ideas , um ,    about the uh , acoustic phonec phonetic approach to speech recognition. Um , so I will be using  these graphical models that  um , that implement the multi - band approach  to recognize a set of intermediate categories that might involve , uh , things like phonetic features  or other  other f feature things that are more closely related to the acoustic signal itself. Um , and the hope in all of this is that by going multi - band and by going into these ,  um intermediate classifications ,  that we can get a system that 's more robust to  to unseen noises , and situations like that. Um , and so , some of the research issues involved in this are ,  um ,   one , what kind of intermediate categories do we need to classify ? Um , another one is  um , what  what other types of structures in these multi - band graphical models should we consider in order to um , combine evidence from  the sub - bands ? And , uh , the third one is how do we  how do we merge all the , uh , information from the individual uh , multi - band classifiers to come up with word  word recognition or  or phone recognition things. Um , so basically that 's  that 's what I 've been doing. And ,

I got two weeks to brush up on d um , presentation stuff and , um ,

Oh , I thought you were finishing your thesis in two weeks.


I got two weeks to brush up on d um , presentation stuff and , um ,

Oh , I thought you were finishing your thesis in two weeks.


Oh , I thought you were finishing your thesis in two weeks.


Are you gonna do any dry runs for your thing ,


Are you gonna do any dry runs for your thing ,

Yes. I , um  I 'm  I 'm gonna do some. Would you be interested ? To help out ?


Yes. I , um  I 'm  I 'm gonna do some. Would you be interested ? To help out ?


Hhh. OK. Uh. Hhh. Let 's see. So we 've got forty minutes left , and it seems like there 's a lot of material. An - any suggestions about where we  where we should go next ?


Hhh. OK. Uh. Hhh. Let 's see. So we 've got forty minutes left , and it seems like there 's a lot of material. An - any suggestions about where we  where we should go next ?

Uh. Do you wanna go , Sunil ? Maybe we 'll just start with you.


Hhh. OK. Uh. Hhh. Let 's see. So we 've got forty minutes left , and it seems like there 's a lot of material. An - any suggestions about where we  where we should go next ?

Uh. Do you wanna go , Sunil ? Maybe we 'll just start with you.

Yeah. But I actually stuck most of this in our m last meeting with Guenter. Um , but I 'll just  Um , so the last week , uh , I showed some results with only SpeechDat - Car which was like some fifty - six percent. And , uh , I didn't h I mean , I  I found that the results  I mean , I wasn't getting that r results on the TI - digit. So I was like looking into " why , what is wrong with the TI - digits ? ". Why  why I was not getting it. And I found that , the noise estimation is a reason for the TI - digits to perform worse than the baseline. So , uh , I actually , picked th I mean , the first thing I did was I just scaled the noise estimate by a factor which is less than one to see if that  because I found there are a lot of zeros in the spectrogram for the TI - digits when I used this approach. So the first thing I did was I just scaled the noise estimate. And I found  So the  the results that I 've shown here are the complete results using the new  Well , the n the new technique is nothing but the noise estimate scaled by a factor of point five. So it 's just an ad - hoc  I mean , some intermediate result , because it 's not optimized for anything. So the results  The trend  the only trend I could see from those results was like the  the p the current noise estimation or the , uh , noise composition scheme is working good for like the car noise type of thing. Because I 've  the only  only  p very good result in the TI - digits is the noise  car noise condition for their test - A , which is like the best I could see that uh , for any non - stationary noise like " Babble " or " Subway " or any  " Street " , some " Restaurant " noise , it 's like  it 's not performing w very well. So , the   So that  that 's the first thing I c uh , I could make out from this stuff. And


Uh. Do you wanna go , Sunil ? Maybe we 'll just start with you.

Yeah. But I actually stuck most of this in our m last meeting with Guenter. Um , but I 'll just  Um , so the last week , uh , I showed some results with only SpeechDat - Car which was like some fifty - six percent. And , uh , I didn't h I mean , I  I found that the results  I mean , I wasn't getting that r results on the TI - digit. So I was like looking into " why , what is wrong with the TI - digits ? ". Why  why I was not getting it. And I found that , the noise estimation is a reason for the TI - digits to perform worse than the baseline. So , uh , I actually , picked th I mean , the first thing I did was I just scaled the noise estimate by a factor which is less than one to see if that  because I found there are a lot of zeros in the spectrogram for the TI - digits when I used this approach. So the first thing I did was I just scaled the noise estimate. And I found  So the  the results that I 've shown here are the complete results using the new  Well , the n the new technique is nothing but the noise estimate scaled by a factor of point five. So it 's just an ad - hoc  I mean , some intermediate result , because it 's not optimized for anything. So the results  The trend  the only trend I could see from those results was like the  the p the current noise estimation or the , uh , noise composition scheme is working good for like the car noise type of thing. Because I 've  the only  only  p very good result in the TI - digits is the noise  car noise condition for their test - A , which is like the best I could see that uh , for any non - stationary noise like " Babble " or " Subway " or any  " Street " , some " Restaurant " noise , it 's like  it 's not performing w very well. So , the   So that  that 's the first thing I c uh , I could make out from this stuff. And

Yeah , I think what is important to see is that there is a big difference between the training modes.


Yeah. But I actually stuck most of this in our m last meeting with Guenter. Um , but I 'll just  Um , so the last week , uh , I showed some results with only SpeechDat - Car which was like some fifty - six percent. And , uh , I didn't h I mean , I  I found that the results  I mean , I wasn't getting that r results on the TI - digit. So I was like looking into " why , what is wrong with the TI - digits ? ". Why  why I was not getting it. And I found that , the noise estimation is a reason for the TI - digits to perform worse than the baseline. So , uh , I actually , picked th I mean , the first thing I did was I just scaled the noise estimate by a factor which is less than one to see if that  because I found there are a lot of zeros in the spectrogram for the TI - digits when I used this approach. So the first thing I did was I just scaled the noise estimate. And I found  So the  the results that I 've shown here are the complete results using the new  Well , the n the new technique is nothing but the noise estimate scaled by a factor of point five. So it 's just an ad - hoc  I mean , some intermediate result , because it 's not optimized for anything. So the results  The trend  the only trend I could see from those results was like the  the p the current noise estimation or the , uh , noise composition scheme is working good for like the car noise type of thing. Because I 've  the only  only  p very good result in the TI - digits is the noise  car noise condition for their test - A , which is like the best I could see that uh , for any non - stationary noise like " Babble " or " Subway " or any  " Street " , some " Restaurant " noise , it 's like  it 's not performing w very well. So , the   So that  that 's the first thing I c uh , I could make out from this stuff. And

Yeah , I think what is important to see is that there is a big difference between the training modes.

Uh - huh. If you have clean training , you get also a fifty percent improvement.


Yeah , I think what is important to see is that there is a big difference between the training modes.

Uh - huh. If you have clean training , you get also a fifty percent improvement.


Uh - huh. If you have clean training , you get also a fifty percent improvement.

But if you have muddy condition training you get only twenty percent.


But if you have muddy condition training you get only twenty percent.


But if you have muddy condition training you get only twenty percent.

Uh , and in that twenty percent @ @ it 's very inconsistent across different noise conditions.


Uh , and in that twenty percent @ @ it 's very inconsistent across different noise conditions.


Uh , and in that twenty percent @ @ it 's very inconsistent across different noise conditions.

So I have like a forty - five  percent for " Car noise " and then there 's a minus five percent for the " Babble " ,


So I have like a forty - five  percent for " Car noise " and then there 's a minus five percent for the " Babble " ,

and there 's this thirty - three for the " Station ". And so  it 's  it 's not  it 's not actually very consistent across. So. The only correlation between the SpeechDat - Car and this performance is the c stationarity of the noise that is there in these conditions and the SpeechDat - Car.


and there 's this thirty - three for the " Station ". And so  it 's  it 's not  it 's not actually very consistent across. So. The only correlation between the SpeechDat - Car and this performance is the c stationarity of the noise that is there in these conditions and the SpeechDat - Car.

And , uh  so  so the overall result is like in the last page , which is like forty - seven , which is still very imbalanced because there are like fifty - six percent on the SpeechDat - Car and thirty - five percent on the TI - digits. And  uh , ps the fifty - six percent is like comparable to what the French Telecom gets , but the thirty - five percent is way off.


and there 's this thirty - three for the " Station ". And so  it 's  it 's not  it 's not actually very consistent across. So. The only correlation between the SpeechDat - Car and this performance is the c stationarity of the noise that is there in these conditions and the SpeechDat - Car.

And , uh  so  so the overall result is like in the last page , which is like forty - seven , which is still very imbalanced because there are like fifty - six percent on the SpeechDat - Car and thirty - five percent on the TI - digits. And  uh , ps the fifty - six percent is like comparable to what the French Telecom gets , but the thirty - five percent is way off.

I 'm sort of confused but  this  I 'm looking on the second page ,


And , uh  so  so the overall result is like in the last page , which is like forty - seven , which is still very imbalanced because there are like fifty - six percent on the SpeechDat - Car and thirty - five percent on the TI - digits. And  uh , ps the fifty - six percent is like comparable to what the French Telecom gets , but the thirty - five percent is way off.

I 'm sort of confused but  this  I 'm looking on the second page ,


And , uh  so  so the overall result is like in the last page , which is like forty - seven , which is still very imbalanced because there are like fifty - six percent on the SpeechDat - Car and thirty - five percent on the TI - digits. And  uh , ps the fifty - six percent is like comparable to what the French Telecom gets , but the thirty - five percent is way off.

I 'm sort of confused but  this  I 'm looking on the second page ,

and it says " fifty percent "  looking in the lower right - hand corner , " fifty percent relative performance ".


I 'm sort of confused but  this  I 'm looking on the second page ,

and it says " fifty percent "  looking in the lower right - hand corner , " fifty percent relative performance ".


and it says " fifty percent "  looking in the lower right - hand corner , " fifty percent relative performance ".


Yeah. For  that 's for the clean training and the noisy testing for the TI - digits.


Yeah. For  that 's for the clean training and the noisy testing for the TI - digits.

So it 's improvement over the baseline mel cepstrum ?


So it 's improvement over the baseline mel cepstrum ?

But the baseline mel cepstrum under those training doesn't do as well I  I 'm  I 'm trying to understand why it 's  it 's eighty percent  That 's an accuracy number , I guess ,


But the baseline mel cepstrum under those training doesn't do as well I  I 'm  I 'm trying to understand why it 's  it 's eighty percent  That 's an accuracy number , I guess ,

right ? So that 's not as good as the one up above.


right ? So that 's not as good as the one up above.

But the fifty is better than the one up above ,


But the fifty is better than the one up above ,


But the fifty is better than the one up above ,

Uh , actually the noise compensation whatever , uh , we are put in it works very well for the high mismatch condition. I mean , it 's consistent in the SpeechDat - Car and in the clean training also it gives it  But this fifty percent is  is that the  the high mismatch performance  equivalent to the high mismatch performance in the speech.


Uh , actually the noise compensation whatever , uh , we are put in it works very well for the high mismatch condition. I mean , it 's consistent in the SpeechDat - Car and in the clean training also it gives it  But this fifty percent is  is that the  the high mismatch performance  equivalent to the high mismatch performance in the speech.

So n s So since the high mismatch performance is much worse to begin with , it 's easier to get a better relative improvement.


Uh , actually the noise compensation whatever , uh , we are put in it works very well for the high mismatch condition. I mean , it 's consistent in the SpeechDat - Car and in the clean training also it gives it  But this fifty percent is  is that the  the high mismatch performance  equivalent to the high mismatch performance in the speech.

So n s So since the high mismatch performance is much worse to begin with , it 's easier to get a better relative improvement.

Yeah. Yeah. I do. Yeah , yeah. So by putting this noise


Uh , actually the noise compensation whatever , uh , we are put in it works very well for the high mismatch condition. I mean , it 's consistent in the SpeechDat - Car and in the clean training also it gives it  But this fifty percent is  is that the  the high mismatch performance  equivalent to the high mismatch performance in the speech.

So n s So since the high mismatch performance is much worse to begin with , it 's easier to get a better relative improvement.

Yeah. Yeah. I do. Yeah , yeah. So by putting this noise

Yeah. Yeah , if we look at the figures on the right , we see that the reference system is very bad.


So n s So since the high mismatch performance is much worse to begin with , it 's easier to get a better relative improvement.

Yeah. Yeah. I do. Yeah , yeah. So by putting this noise

Yeah. Yeah , if we look at the figures on the right , we see that the reference system is very bad.


Yeah. Yeah. I do. Yeah , yeah. So by putting this noise

Yeah. Yeah , if we look at the figures on the right , we see that the reference system is very bad.

Yeah. The reference drops like a very fast


Yeah. Yeah , if we look at the figures on the right , we see that the reference system is very bad.

Yeah. The reference drops like a very fast


Yeah. The reference drops like a very fast

Like for clean  clean training condition.


Like for clean  clean training condition.


This is  this is TI digits  we 're looking at ?


It 's not written anywhere. Yeah , it 's TI - digits. The first r spreadsheet is TI - digits.


It 's not written anywhere. Yeah , it 's TI - digits. The first r spreadsheet is TI - digits.

Mmm. How does clean training do for the , uh , " Car "


Mmm. How does clean training do for the , uh , " Car "


Oh. Still  it still , uh  that  that 's still consistent. I mean , I get the best performance in the case of " Car " , which is the third column in the A condition.


Oh. Still  it still , uh  that  that 's still consistent. I mean , I get the best performance in the case of " Car " , which is the third column in the A condition.

No. I mean , this is added noise. I mean , this is TI - digits. I 'm sorry. I meant  in  in the  in the , uh , multi - language , uh , uh , Finnish and


No. I mean , this is added noise. I mean , this is TI - digits. I 'm sorry. I meant  in  in the  in the , uh , multi - language , uh , uh , Finnish and


So that is the performance for Italian , Finnish and Spanish.


So that is the performance for Italian , Finnish and Spanish.

" Training condition "  Oh , right. So " clean " corresponds to " high mismatch ".


" Training condition "  Oh , right. So " clean " corresponds to " high mismatch ".


Improvement. That 's  " Percentage increase " is the percentage improvement over the baseline.


Improvement. That 's  " Percentage increase " is the percentage improvement over the baseline.

Which means decrease in word error rate ?


Which means decrease in word error rate ?


Which means decrease in word error rate ?

OK , so " percentage increase " means decrease ?


OK , so " percentage increase " means decrease ?


OK , so " percentage increase " means decrease ?

Yeah. The  the w there was a very long discussion about this on  on the  on the , uh , Amsterdam meeting.


Yeah. The  the w there was a very long discussion about this on  on the  on the , uh , Amsterdam meeting.


I  I  I guess you are using finally this  the scheme which they


So. Uh , yeah. So all the hi H M numbers are w very good , in the sense , they are better than what the French Telecom gets. So. But the  the only number that 's still  I mean , which Stephane also got in his result was that medium mismatch of the Finnish , which is very   which is a very strange situation where we used the  we changed the proto for initializing the HMM  I mean , this  this is basically because it gets stuck in some local minimum in the training. That seventy - five point seven nine in the Finnish mismatch which is that  the eleven point nine six what we see.


Yeah  so we start with that different proto and it becomes eighty - eight , which is like some fifty percent improvement.


Yeah  so we start with that different proto and it becomes eighty - eight , which is like some fifty percent improvement.

S Wait a minute. Start with a different what ?


Yeah  so we start with that different proto and it becomes eighty - eight , which is like some fifty percent improvement.

S Wait a minute. Start with a different what ?

Different prototype , which is like a different initialization for the , uh , s transition probabilities. It 's just that right now , the initialization is to stay more in the current state , which is point four point six , right ? Yeah.


S Wait a minute. Start with a different what ?

Different prototype , which is like a different initialization for the , uh , s transition probabilities. It 's just that right now , the initialization is to stay more in the current state , which is point four point six , right ? Yeah.

And if it changes to point five point five , which is equal @ @ for transition and self loop where it becomes eighty - eight percent.


Different prototype , which is like a different initialization for the , uh , s transition probabilities. It 's just that right now , the initialization is to stay more in the current state , which is point four point six , right ? Yeah.

And if it changes to point five point five , which is equal @ @ for transition and self loop where it becomes eighty - eight percent.

Well , but that involves mucking with the back - end ,


And if it changes to point five point five , which is equal @ @ for transition and self loop where it becomes eighty - eight percent.

Well , but that involves mucking with the back - end ,


Well , but that involves mucking with the back - end ,


I mean , it uh , like , i i i It is well known , this  this medium match condition of the Finnish data has some strange effects.


I mean , it uh , like , i i i It is well known , this  this medium match condition of the Finnish data has some strange effects.

It has a very few at  uh , actually , c uh , tran I mean , words also.


It has a very few at  uh , actually , c uh , tran I mean , words also.


It has a very few at  uh , actually , c uh , tran I mean , words also.

It 's a very , very small set , actually.


It 's a very , very small set , actually.


It 's a very , very small set , actually.

There is a l a  There is a lot of  Uh , there are a lot of utterances with music in  with music in the background.


There is a l a  There is a lot of  Uh , there are a lot of utterances with music in  with music in the background.


Yeah. It has some music also. I mean , very horrible music like like I know.


Yeah. It has some music also. I mean , very horrible music like like I know.

So maybe for that one you need a much smarter VAD ? Mmm ,


So maybe for that one you need a much smarter VAD ? Mmm ,

So , that  that 's the  that 's about the results. And , uh , the summary is like  OK. So there are  the other thing what I tried was , which I explained in the last meeting , is using the channel zero for , uh , for both dropping and estimating the noise. And that 's like just to f n get a feel of how good it is. I guess the fifty - six percent improvement in the SpeechDat - Car becomes like sixty - seven percent. Like ten percent better. But that 's  that 's not a  that 's a cheating experiment. So. That 's just  So , m w


So , that  that 's the  that 's about the results. And , uh , the summary is like  OK. So there are  the other thing what I tried was , which I explained in the last meeting , is using the channel zero for , uh , for both dropping and estimating the noise. And that 's like just to f n get a feel of how good it is. I guess the fifty - six percent improvement in the SpeechDat - Car becomes like sixty - seven percent. Like ten percent better. But that 's  that 's not a  that 's a cheating experiment. So. That 's just  So , m w

But the  but the , uh , forty - seven point nine percent which you have now , that 's already a remarkable improvement in comparison to the first proposal.


So , that  that 's the  that 's about the results. And , uh , the summary is like  OK. So there are  the other thing what I tried was , which I explained in the last meeting , is using the channel zero for , uh , for both dropping and estimating the noise. And that 's like just to f n get a feel of how good it is. I guess the fifty - six percent improvement in the SpeechDat - Car becomes like sixty - seven percent. Like ten percent better. But that 's  that 's not a  that 's a cheating experiment. So. That 's just  So , m w

But the  but the , uh , forty - seven point nine percent which you have now , that 's already a remarkable improvement in comparison to the first proposal.

Yeah. So we had forty - four percent in the first proposal.


But the  but the , uh , forty - seven point nine percent which you have now , that 's already a remarkable improvement in comparison to the first proposal.

Yeah. So we had forty - four percent in the first proposal.


Yeah. So we had forty - four percent in the first proposal.


We have f a big im So  the major improvement that we got was in all the high mismatch cases , because all those numbers were in sixties and seventies because we never had any noise compensations.


We have f a big im So  the major improvement that we got was in all the high mismatch cases , because all those numbers were in sixties and seventies because we never had any noise compensations.

So that 's where the biggest improvement came up. Not much in the well match and the medium match and TI - digits also right now. So this is still at three or four percent improvement over the first proposal.


So that 's where the biggest improvement came up. Not much in the well match and the medium match and TI - digits also right now. So this is still at three or four percent improvement over the first proposal.


Then if we can improve the noise estimation , then it should get better.


Then if we can improve the noise estimation , then it should get better.

Yeah , I  I started thinking about also  I mean yeah , uh ,  I discovered the same problem when I started working on  uh , on this Aurora task  almost two years ago , that you have the problem with this mulit a at the beginning we had only this multi condition training of the TI - digits.


Then if we can improve the noise estimation , then it should get better.

Yeah , I  I started thinking about also  I mean yeah , uh ,  I discovered the same problem when I started working on  uh , on this Aurora task  almost two years ago , that you have the problem with this mulit a at the beginning we had only this multi condition training of the TI - digits.

And , uh , I  I found the same problem. Just taking um , what we were used to u  use , I mean , uh , some type of spectral subtraction ,  y  you get even worse results than  the basis


Yeah , I  I started thinking about also  I mean yeah , uh ,  I discovered the same problem when I started working on  uh , on this Aurora task  almost two years ago , that you have the problem with this mulit a at the beginning we had only this multi condition training of the TI - digits.

And , uh , I  I found the same problem. Just taking um , what we were used to u  use , I mean , uh , some type of spectral subtraction ,  y  you get even worse results than  the basis


And , uh , I  I found the same problem. Just taking um , what we were used to u  use , I mean , uh , some type of spectral subtraction ,  y  you get even worse results than  the basis


I  I tried to find an explanation for it ,


I  I tried to find an explanation for it ,

So. Yes. Stephane also has the same experience of using the spectral subtraction right ?


So. Yes. Stephane also has the same experience of using the spectral subtraction right ?


So. Yes. Stephane also has the same experience of using the spectral subtraction right ?

Yeah. So here  here I mean , I found that it 's  if I changed the noise estimate I could get an improvement.


Yeah. So here  here I mean , I found that it 's  if I changed the noise estimate I could get an improvement.


Yeah. So here  here I mean , I found that it 's  if I changed the noise estimate I could get an improvement.

So that 's  so it 's something which I can actually pursue , is the noise estimate.


So that 's  so it 's something which I can actually pursue , is the noise estimate.


So that 's  so it 's something which I can actually pursue , is the noise estimate.

Yeah , I think what you do is in  when  when you have the  the  this multi - condition training mode , um then you have  then you can train models for the speech , for the words , as well as for the pauses where you really have all information about the noise available.


Yeah , I think what you do is in  when  when you have the  the  this multi - condition training mode , um then you have  then you can train models for the speech , for the words , as well as for the pauses where you really have all information about the noise available.


Yeah , I think what you do is in  when  when you have the  the  this multi - condition training mode , um then you have  then you can train models for the speech , for the words , as well as for the pauses where you really have all information about the noise available.

And it was surprising  At the beginning it was not surprising to me that you get really the best results on doing it this way , I mean , in comparison to any type of training on clean data and any type of processing. But it was  So , u u it  it seems to be the best what  wh wh what  what we can do in this moment is multi - condition training. And every when we now start introducing some  some noise reduction technique we  we introduce also somehow artificial distortions.


And it was surprising  At the beginning it was not surprising to me that you get really the best results on doing it this way , I mean , in comparison to any type of training on clean data and any type of processing. But it was  So , u u it  it seems to be the best what  wh wh what  what we can do in this moment is multi - condition training. And every when we now start introducing some  some noise reduction technique we  we introduce also somehow artificial distortions.

And these artificial distortions  uh , I have the feeling that they are the reason why  why we have the problems in this multi - condition training. That means the H M Ms we trained , they are  they are based on Gaussians ,


And these artificial distortions  uh , I have the feeling that they are the reason why  why we have the problems in this multi - condition training. That means the H M Ms we trained , they are  they are based on Gaussians ,

and on modeling Gaussians. And if you  Can I move a little bit with this ? Yeah. And if we introduce now this  this u spectral subtraction , or Wiener filtering stuff  So , usually what you have is maybe , um  I 'm  I 'm showing now an envelope um maybe you 'll  f for this time. So usually you have  maybe in clean condition you have something which looks like this. And if it is noisy it is somewhere here. And then you try to subtract it or Wiener filter or whatever. And what you get is you have always these problems , that you have this  these  these  these zeros in there.


and on modeling Gaussians. And if you  Can I move a little bit with this ? Yeah. And if we introduce now this  this u spectral subtraction , or Wiener filtering stuff  So , usually what you have is maybe , um  I 'm  I 'm showing now an envelope um maybe you 'll  f for this time. So usually you have  maybe in clean condition you have something which looks like this. And if it is noisy it is somewhere here. And then you try to subtract it or Wiener filter or whatever. And what you get is you have always these problems , that you have this  these  these  these zeros in there.

And you have to do something if you get these negative values. I mean , this is your noise estimate and you somehow subtract it or do whatever. Uh , and then you have  And then I think what you do is you introduce some  some artificial distribution in this uh in  in the models. I mean , i you  you train it also this way but , i somehow there is  u u there is no longer a  a Gaussian distribution. It is somehow a strange distribution which we introduce with these  artificial distortions. And  and I was thinking that  that might be the reason why you get these problems in the  especially in the multi - condition training mode.


And you have to do something if you get these negative values. I mean , this is your noise estimate and you somehow subtract it or do whatever. Uh , and then you have  And then I think what you do is you introduce some  some artificial distribution in this uh in  in the models. I mean , i you  you train it also this way but , i somehow there is  u u there is no longer a  a Gaussian distribution. It is somehow a strange distribution which we introduce with these  artificial distortions. And  and I was thinking that  that might be the reason why you get these problems in the  especially in the multi - condition training mode.


And you have to do something if you get these negative values. I mean , this is your noise estimate and you somehow subtract it or do whatever. Uh , and then you have  And then I think what you do is you introduce some  some artificial distribution in this uh in  in the models. I mean , i you  you train it also this way but , i somehow there is  u u there is no longer a  a Gaussian distribution. It is somehow a strange distribution which we introduce with these  artificial distortions. And  and I was thinking that  that might be the reason why you get these problems in the  especially in the multi - condition training mode.

Th - That 's true. Yeah  the c the models are not complex enough to absorb that additional variability that you 're introducing.


Th - That 's true. Yeah  the c the models are not complex enough to absorb that additional variability that you 're introducing.


I also have the feeling that um , the reason ye why it doesn't work is  yeah , that the models are much  are t um , not complex enough. Because I  actually I als always had a good experience with spectral subtraction , just a straight spectral subtraction algorithm when I was using neural networks , big neural networks , which maybe are more able to model strange distributions and


I also have the feeling that um , the reason ye why it doesn't work is  yeah , that the models are much  are t um , not complex enough. Because I  actually I als always had a good experience with spectral subtraction , just a straight spectral subtraction algorithm when I was using neural networks , big neural networks , which maybe are more able to model strange distributions and

But  Yeah. Then I tried the same  exactly the same spectral subtraction algorithm on these Aurora tasks and it simply doesn't work. It 's even  it , uh , hurts even.


But  Yeah. Then I tried the same  exactly the same spectral subtraction algorithm on these Aurora tasks and it simply doesn't work. It 's even  it , uh , hurts even.


But  Yeah. Then I tried the same  exactly the same spectral subtraction algorithm on these Aurora tasks and it simply doesn't work. It 's even  it , uh , hurts even.

We probably should at some point here try the tandem  the  the  the system - two kind of stuff with this , with the spectral subtraction for that reason.


We probably should at some point here try the tandem  the  the  the system - two kind of stuff with this , with the spectral subtraction for that reason.


We probably should at some point here try the tandem  the  the  the system - two kind of stuff with this , with the spectral subtraction for that reason.

Cuz  again , it should do a transformation to a domain where it maybe  looks more Gaussian.


Cuz  again , it should do a transformation to a domain where it maybe  looks more Gaussian.


Cuz  again , it should do a transformation to a domain where it maybe  looks more Gaussian.

Hmm. Yeah , y I  I was  whe w w just yesterday when I was thinking about it  um w what  what we could try to do , or do about it  I mean , if you  if you get at this  in this situation that you get this  this negative values and you simply set it to zero or to a constant or whatever  if we  if we would use there a somehow , um  a random generator which  which has a certain distribution , u not a certain   yeah , a special distribution we should see  we  we have to think about it.


Hmm. Yeah , y I  I was  whe w w just yesterday when I was thinking about it  um w what  what we could try to do , or do about it  I mean , if you  if you get at this  in this situation that you get this  this negative values and you simply set it to zero or to a constant or whatever  if we  if we would use there a somehow , um  a random generator which  which has a certain distribution , u not a certain   yeah , a special distribution we should see  we  we have to think about it.


Hmm. Yeah , y I  I was  whe w w just yesterday when I was thinking about it  um w what  what we could try to do , or do about it  I mean , if you  if you get at this  in this situation that you get this  this negative values and you simply set it to zero or to a constant or whatever  if we  if we would use there a somehow , um  a random generator which  which has a certain distribution , u not a certain   yeah , a special distribution we should see  we  we have to think about it.

And that we , so , introduce again some natural behavior in this trajectory.


And that we , so , introduce again some natural behavior in this trajectory.

Mm - hmm. Very different from speech. Still , I mean , it shouldn't confuse the


And that we , so , introduce again some natural behavior in this trajectory.

Mm - hmm. Very different from speech. Still , I mean , it shouldn't confuse the

Yeah , I mean , similar to what  what you see really u in  in the real um noisy situation.


Mm - hmm. Very different from speech. Still , I mean , it shouldn't confuse the

Yeah , I mean , similar to what  what you see really u in  in the real um noisy situation.


Mm - hmm. Very different from speech. Still , I mean , it shouldn't confuse the

Yeah , I mean , similar to what  what you see really u in  in the real um noisy situation.

Or i in the clean situation. But  but somehow a  a natural distribution.


Yeah , I mean , similar to what  what you see really u in  in the real um noisy situation.

Or i in the clean situation. But  but somehow a  a natural distribution.

But isn't that s again sort of the idea of the additive thing , if it  as  as we had in the J stuff ? I mean , basically if   if you have random data , um , in  in the time domain , then when you look at the s spectrum it 's gonna be pretty flat. And  and ,


Or i in the clean situation. But  but somehow a  a natural distribution.

But isn't that s again sort of the idea of the additive thing , if it  as  as we had in the J stuff ? I mean , basically if   if you have random data , um , in  in the time domain , then when you look at the s spectrum it 's gonna be pretty flat. And  and ,


Or i in the clean situation. But  but somehow a  a natural distribution.

But isn't that s again sort of the idea of the additive thing , if it  as  as we had in the J stuff ? I mean , basically if   if you have random data , um , in  in the time domain , then when you look at the s spectrum it 's gonna be pretty flat. And  and ,

uh , so just add something everywhere rather than just in those places. It 's just a constant , right ?


But isn't that s again sort of the idea of the additive thing , if it  as  as we had in the J stuff ? I mean , basically if   if you have random data , um , in  in the time domain , then when you look at the s spectrum it 's gonna be pretty flat. And  and ,

uh , so just add something everywhere rather than just in those places. It 's just a constant , right ?


uh , so just add something everywhere rather than just in those places. It 's just a constant , right ?

Yeah. I think  e yeah. It 's  it 's just especially in these segments , I mean , you introduce , um , very artificial behavior.


Yeah. I think  e yeah. It 's  it 's just especially in these segments , I mean , you introduce , um , very artificial behavior.


Yeah. I think  e yeah. It 's  it 's just especially in these segments , I mean , you introduce , um , very artificial behavior.

Well , see if you add something everywhere , it has almost no effect up  up  up on  on top. And it  and it  and it has significant effect down there.


Well , see if you add something everywhere , it has almost no effect up  up  up on  on top. And it  and it  and it has significant effect down there.


Hmm. Yeah the  that 's true. That  those  those regions are the cause for this @ @  those negative values or whatever you get.


Hmm. Yeah the  that 's true. That  those  those regions are the cause for this @ @  those negative values or whatever you get.

I mean , we  we could trit uh , we  we could think how w what  what we could try.


I mean , we  we could trit uh , we  we could think how w what  what we could try.


I think when it 's noisy people should just speak up.


I think when it 's noisy people should just speak up.

If we look at the France Telecom proposal , they use some kind of noise addition. They have a random number generator , right ? And they add noise on the trajectory of , uh , the log energy only , right ?


If we look at the France Telecom proposal , they use some kind of noise addition. They have a random number generator , right ? And they add noise on the trajectory of , uh , the log energy only , right ?


C - z C - zero and log energy also , yeah.


C - z C - zero and log energy also , yeah.

Yeah. Um , But I don't know how much effect it  this have , but they do that.


Yeah. Um , But I don't know how much effect it  this have , but they do that.


So it  it  it  it  it is l somehow similar to what


So it  it  it  it  it is l somehow similar to what

I think because they have th log energy , yeah , and then just generate random number. They have some kind of mean and variance , and they add this number to  to the log energy simply. Um


So it  it  it  it  it is l somehow similar to what

I think because they have th log energy , yeah , and then just generate random number. They have some kind of mean and variance , and they add this number to  to the log energy simply. Um

Yeah  the  the log energy , the  after the clean  cleaning up.


I think because they have th log energy , yeah , and then just generate random number. They have some kind of mean and variance , and they add this number to  to the log energy simply. Um

Yeah  the  the log energy , the  after the clean  cleaning up.

So they add a random  random noise to it.


Yeah  the  the log energy , the  after the clean  cleaning up.

So they add a random  random noise to it.


So they add a random  random noise to it.

To the  just the energy , or to the mel  uh , to the mel filter ?


To the  just the energy , or to the mel  uh , to the mel filter ?


So it  Cuz I mean , I think this is most interesting for the mel filters. Right ?


So it  Cuz I mean , I think this is most interesting for the mel filters. Right ?

But  but they do not apply filtering of the log energy or what


But  but they do not apply filtering of the log energy or what


But  but they do not apply filtering of the log energy or what

No  their filter is not M domain. S so they did filter their time signal


No  their filter is not M domain. S so they did filter their time signal


No  their filter is not M domain. S so they did filter their time signal

And then they calculate from this , the log energy


And then they calculate from this , the log energy

Yeah  then after that it is s almost the same as the baseline prop system.


And then they calculate from this , the log energy

Yeah  then after that it is s almost the same as the baseline prop system.

And then the final log energy that they  that they get , that  to the  to that they add some random noise.


Yeah  then after that it is s almost the same as the baseline prop system.

And then the final log energy that they  that they get , that  to the  to that they add some random noise.

Yeah , but again , that 's just log energy as opposed to  filter bank energy.


And then the final log energy that they  that they get , that  to the  to that they add some random noise.

Yeah , but again , that 's just log energy as opposed to  filter bank energy.


Yeah , but again , that 's just log energy as opposed to  filter bank energy.

You know , it 's not the mel filter bank output.


You know , it 's not the mel filter bank output.


You know , it 's not the mel filter bank output.

These are log energy computed from the time s domain signal ,


These are log energy computed from the time s domain signal ,


Maybe it 's just a way to decrease the importance of this particular parameter in the  in the world feature vector cu if you add noise to one of the parameters , you widen the distributions


Maybe it 's just a way to decrease the importance of this particular parameter in the  in the world feature vector cu if you add noise to one of the parameters , you widen the distributions

Becomes flat. The variance , yeah , reduces ,


Becomes flat. The variance , yeah , reduces ,


So it could reduce the dependence on the amplitude and so on. Yeah.


OK. So the other thing is the  I 'm just looking at a little bit on the delay issue where the delay of the system is like a hundred and eighty millisecond. So  I just  just tried another sk system  I mean , another filter which I 've like shown at the end. Which is very similar to the existing uh , filter. Only  Uh , only thing is that the phase is  is like a totally nonlinear phase because it 's a  it 's not a symmetric filter anymore.


OK. So the other thing is the  I 'm just looking at a little bit on the delay issue where the delay of the system is like a hundred and eighty millisecond. So  I just  just tried another sk system  I mean , another filter which I 've like shown at the end. Which is very similar to the existing uh , filter. Only  Uh , only thing is that the phase is  is like a totally nonlinear phase because it 's a  it 's not a symmetric filter anymore.

Yeah  so  so this  this is like  So this makes the delay like zero for LDA because it 's completely causal.