Well , I don't know , perhaps it  perhaps it doesn't hurt because the phase is almost linear but. Um , and so , yeah , for the delay I gave you here , it 's  it 's , uh , computed on the five hertz modulation frequency , which is the  mmm , well , the most important for speech so. Uh , this is the first thing.

So that would be , uh , a reduction of a hundred and thirty - six milliseconds ,


So that would be , uh , a reduction of a hundred and thirty - six milliseconds ,

which , uh  What was the total we ended up with through the whole system ?


which , uh  What was the total we ended up with through the whole system ?


which , uh  What was the total we ended up with through the whole system ?

Yeah , but there are other points actually , uh , which will perhaps add some more delay. Is that some other  other stuff in the process were perhaps not very  um perf well , not very correct , like the downsampling which w was simply dropping frames.


Yeah , but there are other points actually , uh , which will perhaps add some more delay. Is that some other  other stuff in the process were perhaps not very  um perf well , not very correct , like the downsampling which w was simply dropping frames.


Yeah , but there are other points actually , uh , which will perhaps add some more delay. Is that some other  other stuff in the process were perhaps not very  um perf well , not very correct , like the downsampling which w was simply dropping frames.

Um , so we will try also to add a nice downsampling having a filter that  that


Um , so we will try also to add a nice downsampling having a filter that  that

well , a low - pass filter at  at twenty - five hertz. Uh , because wh when  when we look at the LDA filters , well , they are basically low - pass but they leave a lot of what 's above twenty - five hertz.


well , a low - pass filter at  at twenty - five hertz. Uh , because wh when  when we look at the LDA filters , well , they are basically low - pass but they leave a lot of what 's above twenty - five hertz.

Um , and so , yeah , this will be another filter which would add ten milliseconds again.


Um , and so , yeah , this will be another filter which would add ten milliseconds again.

Um , yeah , and then there 's a third thing , is that , um , basically the way on - line normalization was done uh , is just using this recursion on  on the um , um , on the feature stream ,


Um , yeah , and then there 's a third thing , is that , um , basically the way on - line normalization was done uh , is just using this recursion on  on the um , um , on the feature stream ,

and  but this is a filter , so it has also a delay. Uh , and when we look at this filter actually it has a delay of eighty - five milliseconds. So if we


and  but this is a filter , so it has also a delay. Uh , and when we look at this filter actually it has a delay of eighty - five milliseconds. So if we

Yeah. If we want to be very correct , so if we want to  the estimation of the mean t t to  to be  well , the right estimation of the mean , we have to t to take eighty - five milliseconds in the future. Mmm.


and  but this is a filter , so it has also a delay. Uh , and when we look at this filter actually it has a delay of eighty - five milliseconds. So if we

Yeah. If we want to be very correct , so if we want to  the estimation of the mean t t to  to be  well , the right estimation of the mean , we have to t to take eighty - five milliseconds in the future. Mmm.

Hmm ! That 's a little bit of a problem.


Yeah. If we want to be very correct , so if we want to  the estimation of the mean t t to  to be  well , the right estimation of the mean , we have to t to take eighty - five milliseconds in the future. Mmm.

Hmm ! That 's a little bit of a problem.

Yeah. Um , But , well , when we add up everything it 's  it will be alright. We would be at six so , sixty - five , plus ten , plus  for the downsampling , plus eighty - five for the on - line normalization. So it 's


Hmm ! That 's a little bit of a problem.

Yeah. Um , But , well , when we add up everything it 's  it will be alright. We would be at six so , sixty - five , plus ten , plus  for the downsampling , plus eighty - five for the on - line normalization. So it 's

plus  plus eighty for the neural net and PCA.


Yeah. Um , But , well , when we add up everything it 's  it will be alright. We would be at six so , sixty - five , plus ten , plus  for the downsampling , plus eighty - five for the on - line normalization. So it 's

plus  plus eighty for the neural net and PCA.


plus  plus eighty for the neural net and PCA.

So it would be around two hundred and forty  so , well ,


So it would be around two hundred and forty  so , well ,


Two - fifty , unless they changed the rules.


Two - fifty , unless they changed the rules.

Which there is  there 's some discussion of.


Two - fifty , unless they changed the rules.

Which there is  there 's some discussion of.

What were they thinking of changing it to ?


Which there is  there 's some discussion of.

What were they thinking of changing it to ?


What were they thinking of changing it to ?

Uh , well the people who had very low latency want it to be low  uh , very   very very narrow , uh , latency bound. And the people who have longer latency don't. So.


Uh , well the people who had very low latency want it to be low  uh , very   very very narrow , uh , latency bound. And the people who have longer latency don't. So.


Uh , well the people who had very low latency want it to be low  uh , very   very very narrow , uh , latency bound. And the people who have longer latency don't. So.

Unfortunately we 're the main ones with long latency , but


Unfortunately we 're the main ones with long latency , but


Unfortunately we 're the main ones with long latency , but

Yeah , and basically the best proposal had something like thirty or forty milliseconds of latency.


Yeah , and basically the best proposal had something like thirty or forty milliseconds of latency.


Yeah , and basically the best proposal had something like thirty or forty milliseconds of latency.

Yeah , so they were basically  I mean , they were more or less trading computation for performance and we were , uh , trading latency for performance. And they were dealing with noise explicitly and we weren't , and so I think of it as complementary , that if we can put the


Yeah , so they were basically  I mean , they were more or less trading computation for performance and we were , uh , trading latency for performance. And they were dealing with noise explicitly and we weren't , and so I think of it as complementary , that if we can put the


I think the best systems  so , uh , everything that we did in in a way it was  it was just adamantly insisting on going in with a brain damaged system , which is something  actually , we 've done a lot over the last thirteen years. Uh ,  which is we say , well this is the way we should do it. And then we do it. And then someone else does something that 's straight forward. So , w th w this was a test that largely had additive noise and we did  we adde did absolutely nothing explicitly to handle ad additive noise.


I think the best systems  so , uh , everything that we did in in a way it was  it was just adamantly insisting on going in with a brain damaged system , which is something  actually , we 've done a lot over the last thirteen years. Uh ,  which is we say , well this is the way we should do it. And then we do it. And then someone else does something that 's straight forward. So , w th w this was a test that largely had additive noise and we did  we adde did absolutely nothing explicitly to handle ad additive noise.

We just , uh , you know , trained up systems to be more discriminant. And , uh , we did this , uh , RASTA - like filtering which was done in the log domain and was tending to handle convolutional noise. We did  we actually did nothing about additive noise. So , um , the , uh , spectral sub subtraction schemes a couple places did seem to seem to do a nice job. And so , uh , we 're talking about putting  putting some of that in while still keeping some of our stuff. I think you should be able to end up with a system that 's better than both but clearly the way that we 're operating for this other stuff does involved some latency to  to get rid of most of that latency. To get down to forty or fifty milliseconds we 'd have to throw out most of what we 're doing. And  and , uh , I don't think there 's any good reason for it in the application actually. I mean , you 're  you 're  you 're speaking to a recognizer on a remote server and , uh , having a  a  a quarter second for some processing to clean it up. It doesn't seem like it 's that big a deal.


We just , uh , you know , trained up systems to be more discriminant. And , uh , we did this , uh , RASTA - like filtering which was done in the log domain and was tending to handle convolutional noise. We did  we actually did nothing about additive noise. So , um , the , uh , spectral sub subtraction schemes a couple places did seem to seem to do a nice job. And so , uh , we 're talking about putting  putting some of that in while still keeping some of our stuff. I think you should be able to end up with a system that 's better than both but clearly the way that we 're operating for this other stuff does involved some latency to  to get rid of most of that latency. To get down to forty or fifty milliseconds we 'd have to throw out most of what we 're doing. And  and , uh , I don't think there 's any good reason for it in the application actually. I mean , you 're  you 're  you 're speaking to a recognizer on a remote server and , uh , having a  a  a quarter second for some processing to clean it up. It doesn't seem like it 's that big a deal.

These aren't large vocabulary things so the decoder shouldn't take a really long time , and.


We just , uh , you know , trained up systems to be more discriminant. And , uh , we did this , uh , RASTA - like filtering which was done in the log domain and was tending to handle convolutional noise. We did  we actually did nothing about additive noise. So , um , the , uh , spectral sub subtraction schemes a couple places did seem to seem to do a nice job. And so , uh , we 're talking about putting  putting some of that in while still keeping some of our stuff. I think you should be able to end up with a system that 's better than both but clearly the way that we 're operating for this other stuff does involved some latency to  to get rid of most of that latency. To get down to forty or fifty milliseconds we 'd have to throw out most of what we 're doing. And  and , uh , I don't think there 's any good reason for it in the application actually. I mean , you 're  you 're  you 're speaking to a recognizer on a remote server and , uh , having a  a  a quarter second for some processing to clean it up. It doesn't seem like it 's that big a deal.

These aren't large vocabulary things so the decoder shouldn't take a really long time , and.

And I don't think anybody 's gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency.


These aren't large vocabulary things so the decoder shouldn't take a really long time , and.

And I don't think anybody 's gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency.

So. No. What  what does  wa was your experience when you were doing this stuff with , uh , the  the  the surgical , uh , uh , microscopes and so forth. Um , how long was it from when somebody , uh , finished an utterance to when , uh , something started happening ?


These aren't large vocabulary things so the decoder shouldn't take a really long time , and.

And I don't think anybody 's gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency.

So. No. What  what does  wa was your experience when you were doing this stuff with , uh , the  the  the surgical , uh , uh , microscopes and so forth. Um , how long was it from when somebody , uh , finished an utterance to when , uh , something started happening ?

Um , we had a silence detector , so we would look for the end of an utterance based on the silence detector.


And I don't think anybody 's gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency.

So. No. What  what does  wa was your experience when you were doing this stuff with , uh , the  the  the surgical , uh , uh , microscopes and so forth. Um , how long was it from when somebody , uh , finished an utterance to when , uh , something started happening ?

Um , we had a silence detector , so we would look for the end of an utterance based on the silence detector.


So. No. What  what does  wa was your experience when you were doing this stuff with , uh , the  the  the surgical , uh , uh , microscopes and so forth. Um , how long was it from when somebody , uh , finished an utterance to when , uh , something started happening ?

Um , we had a silence detector , so we would look for the end of an utterance based on the silence detector.

And I  I can't remember now off the top of my head how many frames of silence we had to detect before we would declare it to be the end of an utterance.


Um , we had a silence detector , so we would look for the end of an utterance based on the silence detector.

And I  I can't remember now off the top of my head how many frames of silence we had to detect before we would declare it to be the end of an utterance.


And I  I can't remember now off the top of my head how many frames of silence we had to detect before we would declare it to be the end of an utterance.

Um , but it was , uh , I would say it was probably around the order of two hundred and fifty milliseconds.


And I  I can't remember now off the top of my head how many frames of silence we had to detect before we would declare it to be the end of an utterance.

Um , but it was , uh , I would say it was probably around the order of two hundred and fifty milliseconds.

Yeah , and that 's when you 'd start doing things.


Um , but it was , uh , I would say it was probably around the order of two hundred and fifty milliseconds.

Yeah , and that 's when you 'd start doing things.

Yeah , we did the back trace at that point to get the answer.


Um , but it was , uh , I would say it was probably around the order of two hundred and fifty milliseconds.

Yeah , and that 's when you 'd start doing things.

Yeah , we did the back trace at that point to get the answer.

Yeah. Of course that didn't take too long at that point.


Yeah , and that 's when you 'd start doing things.

Yeah , we did the back trace at that point to get the answer.

Yeah. Of course that didn't take too long at that point.


Yeah , we did the back trace at that point to get the answer.

Yeah. Of course that didn't take too long at that point.


Yeah. Of course that didn't take too long at that point.


so you had a  a quarter second delay before , uh , plus some little processing time ,


so you had a  a quarter second delay before , uh , plus some little processing time ,

and then the  the microscope would start moving or something.


and then the  the microscope would start moving or something.


And there 's physical inertia there , so probably the  the motion itself was all


And there 's physical inertia there , so probably the  the motion itself was all

And it felt to , uh , the users that it was instantaneous. I mean , as fast as talking to a person. It  th I don't think anybody ever complained about the delay.


And there 's physical inertia there , so probably the  the motion itself was all

And it felt to , uh , the users that it was instantaneous. I mean , as fast as talking to a person. It  th I don't think anybody ever complained about the delay.

Yeah , so you would think as long as it 's under half a second or something.


And it felt to , uh , the users that it was instantaneous. I mean , as fast as talking to a person. It  th I don't think anybody ever complained about the delay.

Yeah , so you would think as long as it 's under half a second or something.


Yeah , so you would think as long as it 's under half a second or something.


I don't remember the exact numbers but it was something like that.


I don't remember the exact numbers but it was something like that.

I don't think you can really tell. A person  I don't think a person can tell the difference between , uh , you know , a quarter of a second and a hundred milliseconds , and  I 'm not even sure if we can tell the difference between a quarter of a second and half a second.


I don't think you can really tell. A person  I don't think a person can tell the difference between , uh , you know , a quarter of a second and a hundred milliseconds , and  I 'm not even sure if we can tell the difference between a quarter of a second and half a second.


I don't think you can really tell. A person  I don't think a person can tell the difference between , uh , you know , a quarter of a second and a hundred milliseconds , and  I 'm not even sure if we can tell the difference between a quarter of a second and half a second.

Yeah. I mean , basically if you  yeah , if you said , uh , um , " what 's the , uh , uh  what 's the shortest route to the opera ? " and it took half a second to get back to you ,


Yeah. I mean , basically if you  yeah , if you said , uh , um , " what 's the , uh , uh  what 's the shortest route to the opera ? " and it took half a second to get back to you ,


Yeah. I mean , basically if you  yeah , if you said , uh , um , " what 's the , uh , uh  what 's the shortest route to the opera ? " and it took half a second to get back to you ,

I mean ,  it would be f I mean , it might even be too abrupt. You might have to put in a s a s  a delay.


Yeah. I mean , basically if you  yeah , if you said , uh , um , " what 's the , uh , uh  what 's the shortest route to the opera ? " and it took half a second to get back to you ,

I mean ,  it would be f I mean , it might even be too abrupt. You might have to put in a s a s  a delay.

Yeah. I mean , it may feel different than talking to a person


I mean ,  it would be f I mean , it might even be too abrupt. You might have to put in a s a s  a delay.

Yeah. I mean , it may feel different than talking to a person


I mean ,  it would be f I mean , it might even be too abrupt. You might have to put in a s a s  a delay.

Yeah. I mean , it may feel different than talking to a person

because when we talk to each other we tend to step on each other 's utterances. So like if I 'm asking you a question , you may start answering before I 'm even done.


Yeah. I mean , it may feel different than talking to a person

because when we talk to each other we tend to step on each other 's utterances. So like if I 'm asking you a question , you may start answering before I 'm even done.


because when we talk to each other we tend to step on each other 's utterances. So like if I 'm asking you a question , you may start answering before I 'm even done.


Right. Well , anyway , I mean , I think  we could cut  we know what else , we could cut down on the neural net time by  by , uh , playing around a little bit , going more into the past , or something like that. We t we talked about that.


Right. Well , anyway , I mean , I think  we could cut  we know what else , we could cut down on the neural net time by  by , uh , playing around a little bit , going more into the past , or something like that. We t we talked about that.

So is the latency from the neural net caused by how far ahead you 're looking ?


So is the latency from the neural net caused by how far ahead you 're looking ?

And there 's also  well , there 's the neural net and there 's also this , uh , uh , multi - frame , uh , uh , KLT.


And there 's also  well , there 's the neural net and there 's also this , uh , uh , multi - frame , uh , uh , KLT.

Wasn't there  Was it in the , uh , recurrent neural nets where they weren't looking ahead at all ?


And there 's also  well , there 's the neural net and there 's also this , uh , uh , multi - frame , uh , uh , KLT.

Wasn't there  Was it in the , uh , recurrent neural nets where they weren't looking ahead at all ?

They weren't looking ahead much. They p they looked ahead a little bit.


Wasn't there  Was it in the , uh , recurrent neural nets where they weren't looking ahead at all ?

They weren't looking ahead much. They p they looked ahead a little bit.

Yeah. Yeah , I mean , you could do this with a recurrent net. And  and then  But you also could just , um , I mean , we haven't experimented with this but I imagine you could , um , uh , predict a , uh  um , a label , uh , from more in the past than in  than  than in the future. I mean , we 've d we 've done some stuff with that before. I think it  it works OK.


They weren't looking ahead much. They p they looked ahead a little bit.

Yeah. Yeah , I mean , you could do this with a recurrent net. And  and then  But you also could just , um , I mean , we haven't experimented with this but I imagine you could , um , uh , predict a , uh  um , a label , uh , from more in the past than in  than  than in the future. I mean , we 've d we 've done some stuff with that before. I think it  it works OK.


Yeah. Yeah , I mean , you could do this with a recurrent net. And  and then  But you also could just , um , I mean , we haven't experimented with this but I imagine you could , um , uh , predict a , uh  um , a label , uh , from more in the past than in  than  than in the future. I mean , we 've d we 've done some stuff with that before. I think it  it works OK.

We 've always had  usually we used the symmetric windows


We 've always had  usually we used the symmetric windows


We 've always had  usually we used the symmetric windows

Yeah , but we 've  but we played a little bit with  with asymmetric , guys.


Yeah , but we 've  but we played a little bit with  with asymmetric , guys.


Yeah , but we 've  but we played a little bit with  with asymmetric , guys.

You can do it. So. So , that 's what  that 's what you 're busy with , s messing around with this ,


You can do it. So. So , that 's what  that 's what you 're busy with , s messing around with this ,


You can do it. So. So , that 's what  that 's what you 're busy with , s messing around with this ,

Also we were thinking to  to , uh , apply the eh , spectral subtraction from Ericsson


Also we were thinking to  to , uh , apply the eh , spectral subtraction from Ericsson


Also we were thinking to  to , uh , apply the eh , spectral subtraction from Ericsson

and to  to change the contextual KLT for LDA.


and to  to change the contextual KLT for LDA.


KLT , I 'm sorry. Uh , to change and use LDA discriminative.


Well , it 's that by the for the moment we have , uh , something that 's discriminant and nonlinear. And the other is linear but it 's not discriminant at all. Well , it 's it 's a linear transformation , that  Uh


Well , it 's that by the for the moment we have , uh , something that 's discriminant and nonlinear. And the other is linear but it 's not discriminant at all. Well , it 's it 's a linear transformation , that  Uh

So at least just to understand maybe what the difference was between how much you were getting from just putting the frames together and how much you 're getting from the discriminative , what the nonlinearity does for you or doesn't do for you. Just to understand it a little better I guess.


Well , it 's that by the for the moment we have , uh , something that 's discriminant and nonlinear. And the other is linear but it 's not discriminant at all. Well , it 's it 's a linear transformation , that  Uh

So at least just to understand maybe what the difference was between how much you were getting from just putting the frames together and how much you 're getting from the discriminative , what the nonlinearity does for you or doesn't do for you. Just to understand it a little better I guess.

Mmm. Well  uh  yeah. Actually what we want to do , perhaps it 's to replace  to  to have something that 's discriminant but linear , also. And to see if it  if it improves ov over  over the non - discriminant linear transformation.


So at least just to understand maybe what the difference was between how much you were getting from just putting the frames together and how much you 're getting from the discriminative , what the nonlinearity does for you or doesn't do for you. Just to understand it a little better I guess.

Mmm. Well  uh  yeah. Actually what we want to do , perhaps it 's to replace  to  to have something that 's discriminant but linear , also. And to see if it  if it improves ov over  over the non - discriminant linear transformation.

And if the neural net is better than this or , well. So.


Mmm. Well  uh  yeah. Actually what we want to do , perhaps it 's to replace  to  to have something that 's discriminant but linear , also. And to see if it  if it improves ov over  over the non - discriminant linear transformation.

And if the neural net is better than this or , well. So.

Yeah , well , that 's what I meant , is to see whether  whether it  having the neural net really buys you anything.


And if the neural net is better than this or , well. So.

Yeah , well , that 's what I meant , is to see whether  whether it  having the neural net really buys you anything.


And if the neural net is better than this or , well. So.

Yeah , well , that 's what I meant , is to see whether  whether it  having the neural net really buys you anything.

Uh , I mean , it doe did look like it buys you something over just the KLT.


Yeah , well , that 's what I meant , is to see whether  whether it  having the neural net really buys you anything.

Uh , I mean , it doe did look like it buys you something over just the KLT.


Uh , I mean , it doe did look like it buys you something over just the KLT.

But maybe it 's just the discrimination and  and maybe  yeah , maybe the nonlinear discrimination isn't necessary.


But maybe it 's just the discrimination and  and maybe  yeah , maybe the nonlinear discrimination isn't necessary.


Good  good to know. But the other part you were saying was the spectral subtraction , so you just kind of , uh


Good  good to know. But the other part you were saying was the spectral subtraction , so you just kind of , uh

At what stage do you do that ? Do you  you 're doing that , um  ?


Good  good to know. But the other part you were saying was the spectral subtraction , so you just kind of , uh

At what stage do you do that ? Do you  you 're doing that , um  ?

So it would be on the um  on  on the mel frequency bands ,


At what stage do you do that ? Do you  you 're doing that , um  ?

So it would be on the um  on  on the mel frequency bands ,


So it would be on the um  on  on the mel frequency bands ,


we  no  nnn We  we was thinking to do before after VAD or


we  no  nnn We  we was thinking to do before after VAD or

Oh ,  we don't know exactly when it 's better.


Oh ,  we don't know exactly when it 's better.


Oh ,  we don't know exactly when it 's better.

So  so you know that  that  that the way that they 're


So  so you know that  that  that the way that they 're


So  so you know that  that  that the way that they 're

uh , one thing that would be no  good to find out about from this conference call is that what they were talking about , what they 're proposing doing , was having a third party , um , run a good VAD , and  and determine boundaries.


uh , one thing that would be no  good to find out about from this conference call is that what they were talking about , what they 're proposing doing , was having a third party , um , run a good VAD , and  and determine boundaries.


uh , one thing that would be no  good to find out about from this conference call is that what they were talking about , what they 're proposing doing , was having a third party , um , run a good VAD , and  and determine boundaries.

And then given those boundaries , then have everybody do the recognition.


And then given those boundaries , then have everybody do the recognition.

The reason for that was that , um , uh  if some one p one group put in the VAD and another didn't , uh , or one had a better VAD than the other since that  they 're not viewing that as being part of the  the task , and that any  any manufacturer would put a bunch of effort into having some s kind of good speech - silence detection. It still wouldn't be perfect but I mean , e the argument was " let 's not have that be part of this test. " " Let 's  let 's separate that out. " And so , uh , I guess they argued about that yesterday and , yeah , I 'm sorry , I don't  don't know the answer but we should find out. I 'm sure we 'll find out soon what they , uh  what they decided. So , uh  Yeah , so there 's the question of the VAD but otherwise it 's  it 's on the  the , uh  the mel fil filter bank , uh , energies I guess ?


The reason for that was that , um , uh  if some one p one group put in the VAD and another didn't , uh , or one had a better VAD than the other since that  they 're not viewing that as being part of the  the task , and that any  any manufacturer would put a bunch of effort into having some s kind of good speech - silence detection. It still wouldn't be perfect but I mean , e the argument was " let 's not have that be part of this test. " " Let 's  let 's separate that out. " And so , uh , I guess they argued about that yesterday and , yeah , I 'm sorry , I don't  don't know the answer but we should find out. I 'm sure we 'll find out soon what they , uh  what they decided. So , uh  Yeah , so there 's the question of the VAD but otherwise it 's  it 's on the  the , uh  the mel fil filter bank , uh , energies I guess ?


And you 're  you 're subtracting in the  in the  in the  I guess it 's power  power domain , uh , or  or magnitude domain. Probably power domain , right ?


But  yeah , so it 's before everything else ,


But  yeah , so it 's before everything else ,

I mean , if you look at the theory , it 's  it should be in the power domain but  but , uh , I 've seen implementations where people do it in the magnitude domain


I mean , if you look at the theory , it 's  it should be in the power domain but  but , uh , I 've seen implementations where people do it in the magnitude domain


I have asked people why and they shrug their shoulders and say , " oh , it works. " So.


I have asked people why and they shrug their shoulders and say , " oh , it works. " So.

Uh , and there 's this  I guess there 's this mysterious  I mean people who do this a lot I guess have developed little tricks of the trade. I mean , there 's  there 's this , um  you don't just subtract the  the estimate of the noise spectrum. You subtract th that times


Uh , and there 's this  I guess there 's this mysterious  I mean people who do this a lot I guess have developed little tricks of the trade. I mean , there 's  there 's this , um  you don't just subtract the  the estimate of the noise spectrum. You subtract th that times


um , so you have the estimation of the power spectra of the noise , and you multiply this by a factor which is depend dependent on the SNR. So. Well.


um , so you have the estimation of the power spectra of the noise , and you multiply this by a factor which is depend dependent on the SNR. So. Well.

When the speech lev when the signal level is more important , compared to this noise level , the coefficient is small , and around one. But when the power le the s signal level is uh small compared to the noise level , the coefficient is more important. And this reduce actually the music musical noise ,


When the speech lev when the signal level is more important , compared to this noise level , the coefficient is small , and around one. But when the power le the s signal level is uh small compared to the noise level , the coefficient is more important. And this reduce actually the music musical noise ,


When the speech lev when the signal level is more important , compared to this noise level , the coefficient is small , and around one. But when the power le the s signal level is uh small compared to the noise level , the coefficient is more important. And this reduce actually the music musical noise ,

uh which is more important during silence portions ,


uh which is more important during silence portions ,


So there are tricks like this but , mmm.


Is the estimate of the noise spectrum a running estimate ? Or


Well , that 's  I mean , that 's what differs from different  different tasks and different s uh , spectral subtraction methods.


Well , that 's  I mean , that 's what differs from different  different tasks and different s uh , spectral subtraction methods.

I mean , if  if you have , uh , fair assurance that , uh , the noise is  is quite stationary , then the smartest thing to do is use as much data as possible to estimate the noise , get a much better estimate , and subtract it off.


I mean , if  if you have , uh , fair assurance that , uh , the noise is  is quite stationary , then the smartest thing to do is use as much data as possible to estimate the noise , get a much better estimate , and subtract it off.

But if it 's varying at all , which is gonna be the case for almost any real situation , you have to do it on - line , uh , with some forgetting factor or something.


I mean , if  if you have , uh , fair assurance that , uh , the noise is  is quite stationary , then the smartest thing to do is use as much data as possible to estimate the noise , get a much better estimate , and subtract it off.

But if it 's varying at all , which is gonna be the case for almost any real situation , you have to do it on - line , uh , with some forgetting factor or something.

So do you  is there some long window that extends into the past over which you calculate the average ?


But if it 's varying at all , which is gonna be the case for almost any real situation , you have to do it on - line , uh , with some forgetting factor or something.

So do you  is there some long window that extends into the past over which you calculate the average ?

Well , there 's a lot of different ways of computing the noise spectrum. So one of the things that , uh , Hans - Guenter Hirsch did , uh  and pas and other people  actually , he 's  he wasn't the only one I guess , was to , uh , take some period of  of  of speech and in each band , uh , develop a histogram. So , to get a decent histogram of these energies takes at least a few seconds really. But , uh  I mean you can do it with a smaller amount but it 's pretty rough. And , um , in fact I think the NIST standard method of determining signal - to - noise ratio is based on this.


So do you  is there some long window that extends into the past over which you calculate the average ?

Well , there 's a lot of different ways of computing the noise spectrum. So one of the things that , uh , Hans - Guenter Hirsch did , uh  and pas and other people  actually , he 's  he wasn't the only one I guess , was to , uh , take some period of  of  of speech and in each band , uh , develop a histogram. So , to get a decent histogram of these energies takes at least a few seconds really. But , uh  I mean you can do it with a smaller amount but it 's pretty rough. And , um , in fact I think the NIST standard method of determining signal - to - noise ratio is based on this.

So  No , no , it 's based on this kind of method ,


Well , there 's a lot of different ways of computing the noise spectrum. So one of the things that , uh , Hans - Guenter Hirsch did , uh  and pas and other people  actually , he 's  he wasn't the only one I guess , was to , uh , take some period of  of  of speech and in each band , uh , develop a histogram. So , to get a decent histogram of these energies takes at least a few seconds really. But , uh  I mean you can do it with a smaller amount but it 's pretty rough. And , um , in fact I think the NIST standard method of determining signal - to - noise ratio is based on this.

So  No , no , it 's based on this kind of method ,


So  No , no , it 's based on this kind of method ,

this histogram method. So you have a histogram. Now , if you have signal and you have noise , you basically have these two bumps in the histogram , which you could approximate as two Gaussians.


this histogram method. So you have a histogram. Now , if you have signal and you have noise , you basically have these two bumps in the histogram , which you could approximate as two Gaussians.


Right ? And you can use EM to figure out what it is. You know.


Right ? And you can use EM to figure out what it is. You know.

So  so basically now you have this mixture of two Gaussians , you  you n know what they are , and , uh  I mean , sorry , you estimate what they are , and , uh , so this gives you what the signal is and what the noise e energy is in that band in the spectrum. And then you look over the whole thing and now you have a noise spectrum. So , uh , Hans - Guenter Hirsch and others have used that kind of method. And the other thing to do is  which is sort of more trivial and obvious   is to , uh , uh , determine through magical means that  that , uh , there 's no speech in some period , and then see what the spectrum is.


So  so basically now you have this mixture of two Gaussians , you  you n know what they are , and , uh  I mean , sorry , you estimate what they are , and , uh , so this gives you what the signal is and what the noise e energy is in that band in the spectrum. And then you look over the whole thing and now you have a noise spectrum. So , uh , Hans - Guenter Hirsch and others have used that kind of method. And the other thing to do is  which is sort of more trivial and obvious   is to , uh , uh , determine through magical means that  that , uh , there 's no speech in some period , and then see what the spectrum is.

Uh , but , you know , it 's  that  that  that 's tricky to do. It has mistakes. Uh , and if you 've got enough time , uh , this other method appears to be somewhat more reliable. Uh , a variant on that for just determining signal - to - noise ratio is to just , uh  you can do a w a uh  an iterative thing , EM - like thing , to determine means only. I guess it is EM still , but just  just determine the means only. Don't worry about the variances.


Uh , but , you know , it 's  that  that  that 's tricky to do. It has mistakes. Uh , and if you 've got enough time , uh , this other method appears to be somewhat more reliable. Uh , a variant on that for just determining signal - to - noise ratio is to just , uh  you can do a w a uh  an iterative thing , EM - like thing , to determine means only. I guess it is EM still , but just  just determine the means only. Don't worry about the variances.

And then you just use those mean values as being the  the , uh uh signal - to - noise ratio in that band.


Uh , but , you know , it 's  that  that  that 's tricky to do. It has mistakes. Uh , and if you 've got enough time , uh , this other method appears to be somewhat more reliable. Uh , a variant on that for just determining signal - to - noise ratio is to just , uh  you can do a w a uh  an iterative thing , EM - like thing , to determine means only. I guess it is EM still , but just  just determine the means only. Don't worry about the variances.

And then you just use those mean values as being the  the , uh uh signal - to - noise ratio in that band.

But what is the  it seems like this kind of thing could add to the latency. I mean , depending on where the window was that you used to calculate  the signal - to - noise ratio.


And then you just use those mean values as being the  the , uh uh signal - to - noise ratio in that band.

But what is the  it seems like this kind of thing could add to the latency. I mean , depending on where the window was that you used to calculate  the signal - to - noise ratio.


And then you just use those mean values as being the  the , uh uh signal - to - noise ratio in that band.

But what is the  it seems like this kind of thing could add to the latency. I mean , depending on where the window was that you used to calculate  the signal - to - noise ratio.

Not necessarily. Cuz if you don't look into the future , right ?


But what is the  it seems like this kind of thing could add to the latency. I mean , depending on where the window was that you used to calculate  the signal - to - noise ratio.

Not necessarily. Cuz if you don't look into the future , right ?

OK , well that  I guess that was my question ,


Not necessarily. Cuz if you don't look into the future , right ?

OK , well that  I guess that was my question ,


OK , well that  I guess that was my question ,

I mean , if you just  if you  you , uh  a at the beginning you have some


I mean , if you just  if you  you , uh  a at the beginning you have some


It 's an interesting question. I wonder how they did do it ?


It 's an interesting question. I wonder how they did do it ?

Actually , it 's a mmm  If - if you want to have a good estimation on non - stationary noise you have to look in the  in the future. I mean , if you take your window and build your histogram in this window , um , what you can expect is to have an estimation of th of the noise in  in the middle of the window , not at the end. So


Actually , it 's a mmm  If - if you want to have a good estimation on non - stationary noise you have to look in the  in the future. I mean , if you take your window and build your histogram in this window , um , what you can expect is to have an estimation of th of the noise in  in the middle of the window , not at the end. So

but what does  what  what  what does Alcatel do ?


but what does  what  what  what does Alcatel do ?


but what does  what  what  what does Alcatel do ?

The They just look in the past. I guess it works because the noise are , uh pret uh , almost stationary


The They just look in the past. I guess it works because the noise are , uh pret uh , almost stationary


Yeah , y I mean , you 're talking about non - stationary noise but I think that spectral subtraction is rarely  is  is not gonna work really well for  for non - stationary noise ,


Yeah , y I mean , you 're talking about non - stationary noise but I think that spectral subtraction is rarely  is  is not gonna work really well for  for non - stationary noise ,

Well , if y if you have a good estimation of the noise ,


Well , if y if you have a good estimation of the noise ,


Yeah. So  so I think that  that what  what is  wh what 's more common is that you 're going to be helped with r slowly varying or stationary noise.


Yeah. So  so I think that  that what  what is  wh what 's more common is that you 're going to be helped with r slowly varying or stationary noise.

That 's what spectral subtraction will help with , practically speaking.


That 's what spectral subtraction will help with , practically speaking.

If it varies a lot , to get a If  if  to get a good estimate you need a few seconds of speech , even if it 's centered , right ?


If it varies a lot , to get a If  if  to get a good estimate you need a few seconds of speech , even if it 's centered , right ?

if you need a few seconds to get a decent estimate but it 's changed a lot in a few seconds , then it , you know , i it 's kind of a problem.


if you need a few seconds to get a decent estimate but it 's changed a lot in a few seconds , then it , you know , i it 's kind of a problem.

I mean , imagine e five hertz is the middle of the  of the speech modulation spectrum ,


I mean , imagine e five hertz is the middle of the  of the speech modulation spectrum ,

right ? So imagine a jack hammer going at five hertz.


right ? So imagine a jack hammer going at five hertz.


right ? So imagine a jack hammer going at five hertz.

So in this case , yeah , sure , you cannot


So in this case , yeah , sure , you cannot


So in this case , yeah , sure , you cannot

But I think y um , Hirsch does experiment with windows of like between five hundred milliseconds and one second. And well , five hundred wa was not so bad. I mean and he worked on non - stationary noises , like noise modulated with well , wi with amplitude modulations and things like that ,


So in this case , yeah , sure , you cannot

But I think y um , Hirsch does experiment with windows of like between five hundred milliseconds and one second. And well , five hundred wa was not so bad. I mean and he worked on non - stationary noises , like noise modulated with well , wi with amplitude modulations and things like that ,

Were his , uh , windows centered around the


But I think y um , Hirsch does experiment with windows of like between five hundred milliseconds and one second. And well , five hundred wa was not so bad. I mean and he worked on non - stationary noises , like noise modulated with well , wi with amplitude modulations and things like that ,

Were his , uh , windows centered around the

and  But  Um , yeah. Well , I think  Yeah. Well , in  in the paper he showed that actually the estimation of the noise is  is delayed. Well , it 's  there is  you  you have to center the window , yeah.


Were his , uh , windows centered around the

and  But  Um , yeah. Well , I think  Yeah. Well , in  in the paper he showed that actually the estimation of the noise is  is delayed. Well , it 's  there is  you  you have to center the window , yeah.


and  But  Um , yeah. Well , I think  Yeah. Well , in  in the paper he showed that actually the estimation of the noise is  is delayed. Well , it 's  there is  you  you have to center the window , yeah.

No , I understand it 's better to do but I just think that  that , uh , for real noises wh what  what 's most likely to happen is that there 'll be some things that are relatively stationary


No , I understand it 's better to do but I just think that  that , uh , for real noises wh what  what 's most likely to happen is that there 'll be some things that are relatively stationary


No , I understand it 's better to do but I just think that  that , uh , for real noises wh what  what 's most likely to happen is that there 'll be some things that are relatively stationary

where you can use one or another spectral subtraction thing


where you can use one or another spectral subtraction thing

and other things where it 's not so stationary and  I mean , you can always pick something that  that falls between your methods ,


and other things where it 's not so stationary and  I mean , you can always pick something that  that falls between your methods ,

uh , uh , but I don't know if , you know , if sinusoidally , uh , modul amplitude modulated noise is  is sort of a big problem in  in in  practice.


uh , uh , but I don't know if , you know , if sinusoidally , uh , modul amplitude modulated noise is  is sort of a big problem in  in in  practice.


uh , uh , but I don't know if , you know , if sinusoidally , uh , modul amplitude modulated noise is  is sort of a big problem in  in in  practice.

We could probably get a really good estimate of the noise if we just went to the noise files , and built the averages from them.


We could probably get a really good estimate of the noise if we just went to the noise files , and built the averages from them.


But if the  if the noise is stationary perhaps you don't even need some kind of noise estimation algorithm.


But if the  if the noise is stationary perhaps you don't even need some kind of noise estimation algorithm.

We just take th th th the beginning of the utterance and


We just take th th th the beginning of the utterance and

I I know p I don't know if people tried this for Aurora.


I I know p I don't know if people tried this for Aurora.

Well , everybody seems to use some kind of adaptive , well , scheme


Well , everybody seems to use some kind of adaptive , well , scheme


Right , the word " stationary " is  has a very precise statistical meaning. But , you know , in  in signal - processing really what we 're talking about I think is things that change slowly , uh , compared with our  our processing techniques.


Right , the word " stationary " is  has a very precise statistical meaning. But , you know , in  in signal - processing really what we 're talking about I think is things that change slowly , uh , compared with our  our processing techniques.

So if you 're driving along in a car I  I would think that most of the time the nature of the noise is going to change relatively slowly. It 's not gonna stay absolute the same. If you  if you check it out , uh , five minutes later you may be in a different part of the road


So if you 're driving along in a car I  I would think that most of the time the nature of the noise is going to change relatively slowly. It 's not gonna stay absolute the same. If you  if you check it out , uh , five minutes later you may be in a different part of the road

or whatever. But it 's  it 's  i i i using the local characteristics in time , is probably going to work pretty well.


or whatever. But it 's  it 's  i i i using the local characteristics in time , is probably going to work pretty well.

But you could get hurt a lot if you just took some something from the beginning of all the speech , of , you know , an hour of speech and then later


But you could get hurt a lot if you just took some something from the beginning of all the speech , of , you know , an hour of speech and then later

Uh , so they may be  you know , may be overly , uh , complicated for  for this test but  but  but , uh , I don't know. But what you 're saying , you know , makes sense , though. I mean , if possible you shouldn't  you should  you should make it , uh , the center of the  center of the window. But  uh , we 're already having problems with these delay , uh   delay issues.


Uh , so they may be  you know , may be overly , uh , complicated for  for this test but  but  but , uh , I don't know. But what you 're saying , you know , makes sense , though. I mean , if possible you shouldn't  you should  you should make it , uh , the center of the  center of the window. But  uh , we 're already having problems with these delay , uh   delay issues.

So , uh , we 'll have to figure ways without it. Um ,


Uh , so they may be  you know , may be overly , uh , complicated for  for this test but  but  but , uh , I don't know. But what you 're saying , you know , makes sense , though. I mean , if possible you shouldn't  you should  you should make it , uh , the center of the  center of the window. But  uh , we 're already having problems with these delay , uh   delay issues.

So , uh , we 'll have to figure ways without it. Um ,

If they 're going to provide a , uh , voice activity detector that will tell you the boundaries of the speech , then , couldn't you just go outside those boundaries and do your estimate there ?


So , uh , we 'll have to figure ways without it. Um ,

If they 're going to provide a , uh , voice activity detector that will tell you the boundaries of the speech , then , couldn't you just go outside those boundaries and do your estimate there ?

Oh , yeah. You bet. Yeah. So I  I imagine that 's what they 're doing , right ? Is they 're  they 're probably looking in nonspeech sections and getting some , uh


So , uh , we 'll have to figure ways without it. Um ,

If they 're going to provide a , uh , voice activity detector that will tell you the boundaries of the speech , then , couldn't you just go outside those boundaries and do your estimate there ?

Oh , yeah. You bet. Yeah. So I  I imagine that 's what they 're doing , right ? Is they 're  they 're probably looking in nonspeech sections and getting some , uh

Yeah , they have some kind of threshold on  on the previous estimate , and  So. Yeah. I think. Yeah , I think Ericsson used this kind of threshold. Yeah , so , they h they have an estimate of the noise level and they put a threshold like six or ten DB above , and what 's under this threshold is used to update the estimate. Is  is that right


If they 're going to provide a , uh , voice activity detector that will tell you the boundaries of the speech , then , couldn't you just go outside those boundaries and do your estimate there ?

Oh , yeah. You bet. Yeah. So I  I imagine that 's what they 're doing , right ? Is they 're  they 're probably looking in nonspeech sections and getting some , uh

Yeah , they have some kind of threshold on  on the previous estimate , and  So. Yeah. I think. Yeah , I think Ericsson used this kind of threshold. Yeah , so , they h they have an estimate of the noise level and they put a threshold like six or ten DB above , and what 's under this threshold is used to update the estimate. Is  is that right


Oh , yeah. You bet. Yeah. So I  I imagine that 's what they 're doing , right ? Is they 're  they 're probably looking in nonspeech sections and getting some , uh

Yeah , they have some kind of threshold on  on the previous estimate , and  So. Yeah. I think. Yeah , I think Ericsson used this kind of threshold. Yeah , so , they h they have an estimate of the noise level and they put a threshold like six or ten DB above , and what 's under this threshold is used to update the estimate. Is  is that right


Yeah , they have some kind of threshold on  on the previous estimate , and  So. Yeah. I think. Yeah , I think Ericsson used this kind of threshold. Yeah , so , they h they have an estimate of the noise level and they put a threshold like six or ten DB above , and what 's under this threshold is used to update the estimate. Is  is that right


Yeah. It 's like saying what 's under the threshold is silence ,


Does France Telecom do th do the same thing ? More or less ?


Does France Telecom do th do the same thing ? More or less ?

OK. Um , OK , if we 're  we 're done  done with that , uh , let 's see. Uh , maybe we can talk about a couple other things briefly , just , uh , things that  that we 've been chatting about but haven't made it into these meetings yet. So you 're coming up with your quals proposal , and , uh  Wanna just give a two three minute summary of what you 're planning on doing ?


OK. Um , OK , if we 're  we 're done  done with that , uh , let 's see. Uh , maybe we can talk about a couple other things briefly , just , uh , things that  that we 've been chatting about but haven't made it into these meetings yet. So you 're coming up with your quals proposal , and , uh  Wanna just give a two three minute summary of what you 're planning on doing ?

Oh , um , two , three , it can be shorter than that.


OK. Um , OK , if we 're  we 're done  done with that , uh , let 's see. Uh , maybe we can talk about a couple other things briefly , just , uh , things that  that we 've been chatting about but haven't made it into these meetings yet. So you 're coming up with your quals proposal , and , uh  Wanna just give a two three minute summary of what you 're planning on doing ?

Oh , um , two , three , it can be shorter than that.

Um. Well , I 've  I 've talked to some of you already. Um , but I 'm , uh , looking into extending the work done by Larry Saul and John Allen and uh Mazin Rahim. Um , they  they have a system that 's , uh , a multi - band , um , system but their multi - band is  is a little different than the way that we 've been doing multi - band in the past , where um  Where we 've been @ @  uh taking  um   sub - band features and i training up these neural nets and  on  on phonetic targets , and then combining them some somehow down the line , um , they 're  they 're taking sub - band features and , um , training up a detector that detects for , um , these phonetic features for example , um , he presents um , uh , a detector to detect sonorance. And so what  what it basically is  is , um  it 's  there 's  at the lowest level , there  it 's  it 's an OR ga I mean , it 's an AND gate. So , uh , on each sub - band you have several independent tests , to test whether um , there 's the existence of sonorance in a sub - band. And then , um , it c it 's combined by a soft AND gate. And at the  at the higher level , for every  if , um  The higher level there 's a soft OR gate. Uh , so if  if this detector detects um , the presence of  of sonorance in any of the sub - bands , then the detect uh , the OR gate at the top says , " OK , well this frame has evidence of sonorance. "


Oh , um , two , three , it can be shorter than that.

Um. Well , I 've  I 've talked to some of you already. Um , but I 'm , uh , looking into extending the work done by Larry Saul and John Allen and uh Mazin Rahim. Um , they  they have a system that 's , uh , a multi - band , um , system but their multi - band is  is a little different than the way that we 've been doing multi - band in the past , where um  Where we 've been @ @  uh taking  um   sub - band features and i training up these neural nets and  on  on phonetic targets , and then combining them some somehow down the line , um , they 're  they 're taking sub - band features and , um , training up a detector that detects for , um , these phonetic features for example , um , he presents um , uh , a detector to detect sonorance. And so what  what it basically is  is , um  it 's  there 's  at the lowest level , there  it 's  it 's an OR ga I mean , it 's an AND gate. So , uh , on each sub - band you have several independent tests , to test whether um , there 's the existence of sonorance in a sub - band. And then , um , it c it 's combined by a soft AND gate. And at the  at the higher level , for every  if , um  The higher level there 's a soft OR gate. Uh , so if  if this detector detects um , the presence of  of sonorance in any of the sub - bands , then the detect uh , the OR gate at the top says , " OK , well this frame has evidence of sonorance. "

What are  what are some of the low level detectors that they use ?


Um. Well , I 've  I 've talked to some of you already. Um , but I 'm , uh , looking into extending the work done by Larry Saul and John Allen and uh Mazin Rahim. Um , they  they have a system that 's , uh , a multi - band , um , system but their multi - band is  is a little different than the way that we 've been doing multi - band in the past , where um  Where we 've been @ @  uh taking  um   sub - band features and i training up these neural nets and  on  on phonetic targets , and then combining them some somehow down the line , um , they 're  they 're taking sub - band features and , um , training up a detector that detects for , um , these phonetic features for example , um , he presents um , uh , a detector to detect sonorance. And so what  what it basically is  is , um  it 's  there 's  at the lowest level , there  it 's  it 's an OR ga I mean , it 's an AND gate. So , uh , on each sub - band you have several independent tests , to test whether um , there 's the existence of sonorance in a sub - band. And then , um , it c it 's combined by a soft AND gate. And at the  at the higher level , for every  if , um  The higher level there 's a soft OR gate. Uh , so if  if this detector detects um , the presence of  of sonorance in any of the sub - bands , then the detect uh , the OR gate at the top says , " OK , well this frame has evidence of sonorance. "

What are  what are some of the low level detectors that they use ?

And these are all  Oh , OK. Well , the low level detectors are logistic regressions. Um , and the , uh


Um. Well , I 've  I 've talked to some of you already. Um , but I 'm , uh , looking into extending the work done by Larry Saul and John Allen and uh Mazin Rahim. Um , they  they have a system that 's , uh , a multi - band , um , system but their multi - band is  is a little different than the way that we 've been doing multi - band in the past , where um  Where we 've been @ @  uh taking  um   sub - band features and i training up these neural nets and  on  on phonetic targets , and then combining them some somehow down the line , um , they 're  they 're taking sub - band features and , um , training up a detector that detects for , um , these phonetic features for example , um , he presents um , uh , a detector to detect sonorance. And so what  what it basically is  is , um  it 's  there 's  at the lowest level , there  it 's  it 's an OR ga I mean , it 's an AND gate. So , uh , on each sub - band you have several independent tests , to test whether um , there 's the existence of sonorance in a sub - band. And then , um , it c it 's combined by a soft AND gate. And at the  at the higher level , for every  if , um  The higher level there 's a soft OR gate. Uh , so if  if this detector detects um , the presence of  of sonorance in any of the sub - bands , then the detect uh , the OR gate at the top says , " OK , well this frame has evidence of sonorance. "

What are  what are some of the low level detectors that they use ?

And these are all  Oh , OK. Well , the low level detectors are logistic regressions. Um , and the , uh

So that , by the way , basically is a  is one of the units in our  in our  our neural network.


What are  what are some of the low level detectors that they use ?

And these are all  Oh , OK. Well , the low level detectors are logistic regressions. Um , and the , uh

So that , by the way , basically is a  is one of the units in our  in our  our neural network.


And these are all  Oh , OK. Well , the low level detectors are logistic regressions. Um , and the , uh

So that , by the way , basically is a  is one of the units in our  in our  our neural network.

So that 's all it is. It 's a sig it 's a sigmoid ,


So that , by the way , basically is a  is one of the units in our  in our  our neural network.

So that 's all it is. It 's a sig it 's a sigmoid ,


So that 's all it is. It 's a sig it 's a sigmoid ,


Right. Yeah , so he uses , um , an EM algorithm to  to um train up these um parameters for the logistic regression.


Right. Yeah , so he uses , um , an EM algorithm to  to um train up these um parameters for the logistic regression.

so I was using EM to get the targets. So  so you have this  this  this AND gate  what we were calling an AND gate , but it 's a product  product rule thing at the output. And then he uses , uh , i u and then feeding into that are  I 'm sorry , there 's  it 's an OR at the output , isn't it ? Yeah ,


so I was using EM to get the targets. So  so you have this  this  this AND gate  what we were calling an AND gate , but it 's a product  product rule thing at the output. And then he uses , uh , i u and then feeding into that are  I 'm sorry , there 's  it 's an OR at the output , isn't it ? Yeah ,


so I was using EM to get the targets. So  so you have this  this  this AND gate  what we were calling an AND gate , but it 's a product  product rule thing at the output. And then he uses , uh , i u and then feeding into that are  I 'm sorry , there 's  it 's an OR at the output , isn't it ? Yeah ,

so that 's the product. And then , um , then he has each of these AND things. And , um , but  so they 're little neural  neural units. Um , and , um , they have to have targets. And so the targets come from EM.


so I was using EM to get the targets. So  so you have this  this  this AND gate  what we were calling an AND gate , but it 's a product  product rule thing at the output. And then he uses , uh , i u and then feeding into that are  I 'm sorry , there 's  it 's an OR at the output , isn't it ? Yeah ,

so that 's the product. And then , um , then he has each of these AND things. And , um , but  so they 're little neural  neural units. Um , and , um , they have to have targets. And so the targets come from EM.

And so are each of these , low level detectors   are they , uh  are these something that you decide ahead of time , like " I 'm going to look for this particular feature or I 'm going to look at this frequency , " or  What  what  what are they looking at ?


so that 's the product. And then , um , then he has each of these AND things. And , um , but  so they 're little neural  neural units. Um , and , um , they have to have targets. And so the targets come from EM.

And so are each of these , low level detectors   are they , uh  are these something that you decide ahead of time , like " I 'm going to look for this particular feature or I 'm going to look at this frequency , " or  What  what  what are they looking at ?


And so are each of these , low level detectors   are they , uh  are these something that you decide ahead of time , like " I 'm going to look for this particular feature or I 'm going to look at this frequency , " or  What  what  what are they looking at ?

Uh Right , so the  OK , so at each for each sub - band  there are basically , uh , several measures of SNR and  and correlation.


Uh Right , so the  OK , so at each for each sub - band  there are basically , uh , several measures of SNR and  and correlation.


Uh Right , so the  OK , so at each for each sub - band  there are basically , uh , several measures of SNR and  and correlation.

Um , um and he said there 's like twenty of these per  per sub - band. Um , and for  for every s every sub - band , e you  you just pick ahead of time , um , " I 'm going to have like five  i independent logistic tests. "


Um , um and he said there 's like twenty of these per  per sub - band. Um , and for  for every s every sub - band , e you  you just pick ahead of time , um , " I 'm going to have like five  i independent logistic tests. "

And you initialize these parameters , um , in some  some way and use EM to come up with your training targets for a  for the  the low - level detectors.


And you initialize these parameters , um , in some  some way and use EM to come up with your training targets for a  for the  the low - level detectors.

And then , once you get that done , you  you  you train the whole  whole thing on maximum likelihood. Um , and h he shows that using this  this method to detect sonorance is it 's very robust compared to , um   to typical , uh , full - band Gaussian mixtures um estimations of  of sonorance.


And then , once you get that done , you  you  you train the whole  whole thing on maximum likelihood. Um , and h he shows that using this  this method to detect sonorance is it 's very robust compared to , um   to typical , uh , full - band Gaussian mixtures um estimations of  of sonorance.

And , uh so  so that 's just  that 's just one detector. So you can imagine building many of these detectors on different features. You get enough of these detectors together , um , then you have enough information to do , um , higher level discrimination , for example , discriminating between phones


And , uh so  so that 's just  that 's just one detector. So you can imagine building many of these detectors on different features. You get enough of these detectors together , um , then you have enough information to do , um , higher level discrimination , for example , discriminating between phones

and then you keep working your way up until you  you build a full recognizer.


and then you keep working your way up until you  you build a full recognizer.

So , um , that 's  that 's the direction which I 'm  I 'm thinking about going in my quals.


So , um , that 's  that 's the direction which I 'm  I 'm thinking about going in my quals.

You know , it has a number of properties that I really liked. I mean , one is the going towards , um , using narrow band information for , uh , ph phonetic features of some sort rather than just , uh , immediately going for the  the typical sound units.


You know , it has a number of properties that I really liked. I mean , one is the going towards , um , using narrow band information for , uh , ph phonetic features of some sort rather than just , uh , immediately going for the  the typical sound units.

Another thing I like about it is that you t this thing is going to be trained  explicitly trained for a product of errors rule , which is what , uh , Allen keeps pointing out that Fletcher observed in the twenties ,


Another thing I like about it is that you t this thing is going to be trained  explicitly trained for a product of errors rule , which is what , uh , Allen keeps pointing out that Fletcher observed in the twenties ,

uh , for people listening to narrow band stuff. That 's Friday 's talk , by the way. And then , um , Uh , the third thing I like about it is , uh , and we 've played around with this in a different kind of way a little bit but it hasn't been our dominant way of  of operating anything , um , this issue of where the targets come from. So in our case when we 've been training it multi - band things , the way we get the targets for the individual bands is , uh , that we get the phonetic label  for the sound there


uh , for people listening to narrow band stuff. That 's Friday 's talk , by the way. And then , um , Uh , the third thing I like about it is , uh , and we 've played around with this in a different kind of way a little bit but it hasn't been our dominant way of  of operating anything , um , this issue of where the targets come from. So in our case when we 've been training it multi - band things , the way we get the targets for the individual bands is , uh , that we get the phonetic label  for the sound there

and we say , " OK , we train every  " What this is saying is , OK , that 's maybe what our ultimate goal is  or not ultimate but penultimate  goal is getting these  these small sound units. But  but , um , along the way how much should we , uh  uh , what should we be training these intermediate things for ? I mean , because , uh , we don't know uh , that this is a particularly good feature. I mean , there 's no way , uh  someone in the audience yesterday was asking , " well couldn't you have people go through and mark the individual bands and say where the  where it was sonorant or not ? "


and we say , " OK , we train every  " What this is saying is , OK , that 's maybe what our ultimate goal is  or not ultimate but penultimate  goal is getting these  these small sound units. But  but , um , along the way how much should we , uh  uh , what should we be training these intermediate things for ? I mean , because , uh , we don't know uh , that this is a particularly good feature. I mean , there 's no way , uh  someone in the audience yesterday was asking , " well couldn't you have people go through and mark the individual bands and say where the  where it was sonorant or not ? "

But , you know , I think having a bunch of people listening to critical band wide ,  uh , chunks of speech trying to determine whether   I think it 'd be impossible.


But , you know , I think having a bunch of people listening to critical band wide ,  uh , chunks of speech trying to determine whether   I think it 'd be impossible.

It 's all gonna sound like  like sine waves to you , more or less.


It 's all gonna sound like  like sine waves to you , more or less.

I mean  Well not I mean , it 's g all g narrow band uh , i I m I think it 's very hard for someone to  to  a person to make that determination. So , um , um , we don't really know how those should be labeled. It could sh be that you should , um , not be paying that much attention to , uh , certain bands for certain sounds , uh , in order to get the best result.


I mean  Well not I mean , it 's g all g narrow band uh , i I m I think it 's very hard for someone to  to  a person to make that determination. So , um , um , we don't really know how those should be labeled. It could sh be that you should , um , not be paying that much attention to , uh , certain bands for certain sounds , uh , in order to get the best result.

So , um , what we have been doing there , just sort of mixing it all together , is certainly much  much cruder than that. We trained these things up on the  on the , uh the final label. Now we have I guess done experiments  you 've probably done stuff where you have , um , done separate , uh , Viterbis on the different


I mean  Well not I mean , it 's g all g narrow band uh , i I m I think it 's very hard for someone to  to  a person to make that determination. So , um , um , we don't really know how those should be labeled. It could sh be that you should , um , not be paying that much attention to , uh , certain bands for certain sounds , uh , in order to get the best result.

So , um , what we have been doing there , just sort of mixing it all together , is certainly much  much cruder than that. We trained these things up on the  on the , uh the final label. Now we have I guess done experiments  you 've probably done stuff where you have , um , done separate , uh , Viterbis on the different

Yeah. Forced alignment on the sub - band labels ?


So , um , what we have been doing there , just sort of mixing it all together , is certainly much  much cruder than that. We trained these things up on the  on the , uh the final label. Now we have I guess done experiments  you 've probably done stuff where you have , um , done separate , uh , Viterbis on the different

Yeah. Forced alignment on the sub - band labels ?


Yeah. Forced alignment on the sub - band labels ?

You 've done that. Did  did that help at all ?


You 've done that. Did  did that help at all ?

Um , it helps for one or t one iteration but um , anything after that it doesn't help.


You 've done that. Did  did that help at all ?

Um , it helps for one or t one iteration but um , anything after that it doesn't help.

So  so that may or may t it  that aspect of what he 's doing may or may not be helpful because in a sense that 's the same sort of thing. You 're taking global information and determining what you  how you should  But this is  this is , uh , I th I think a little more direct.


You 've done that. Did  did that help at all ?

Um , it helps for one or t one iteration but um , anything after that it doesn't help.

So  so that may or may t it  that aspect of what he 's doing may or may not be helpful because in a sense that 's the same sort of thing. You 're taking global information and determining what you  how you should  But this is  this is , uh , I th I think a little more direct.

How did they measure the performance of their detector ?


Um , it helps for one or t one iteration but um , anything after that it doesn't help.

So  so that may or may t it  that aspect of what he 's doing may or may not be helpful because in a sense that 's the same sort of thing. You 're taking global information and determining what you  how you should  But this is  this is , uh , I th I think a little more direct.

How did they measure the performance of their detector ?

And  Well , he 's look he 's just actually looking at , uh , the confusions between sonorant and non - sonorant.


So  so that may or may t it  that aspect of what he 's doing may or may not be helpful because in a sense that 's the same sort of thing. You 're taking global information and determining what you  how you should  But this is  this is , uh , I th I think a little more direct.

How did they measure the performance of their detector ?

And  Well , he 's look he 's just actually looking at , uh , the confusions between sonorant and non - sonorant.


How did they measure the performance of their detector ?

And  Well , he 's look he 's just actually looking at , uh , the confusions between sonorant and non - sonorant.

So he hasn't applied it to recognition or if he did he didn't talk about it. It 's  it 's just  And one of the concerns in the audience , actually , was that  that , um , the , uh , uh  he  he did a comparison to , uh , you know , our old foil , the  the nasty old standard recognizer with  mel  mel filter bank at the front , and H M Ms , and  and so forth. And , um , it didn't do nearly as well , especially in  in noise. But the  one of the good questions in the audience was , well , yeah , but that wasn't trained for that. I mean , this use of a very smooth , uh , spectral envelope is something that , you know , has evolved as being generally a good thing for speech recognition but if you knew that what you were gonna do is detect sonorants or not  So sonorants and non - sonorants is  is  is almost like voiced - unvoiced , except I guess that the voiced stops are  are also called " obstruents ". Uh , so it 's  it 's  uh , but with the exception of the stops I guess it 's pretty much the same as voiced - unvoiced ,


And  Well , he 's look he 's just actually looking at , uh , the confusions between sonorant and non - sonorant.

So he hasn't applied it to recognition or if he did he didn't talk about it. It 's  it 's just  And one of the concerns in the audience , actually , was that  that , um , the , uh , uh  he  he did a comparison to , uh , you know , our old foil , the  the nasty old standard recognizer with  mel  mel filter bank at the front , and H M Ms , and  and so forth. And , um , it didn't do nearly as well , especially in  in noise. But the  one of the good questions in the audience was , well , yeah , but that wasn't trained for that. I mean , this use of a very smooth , uh , spectral envelope is something that , you know , has evolved as being generally a good thing for speech recognition but if you knew that what you were gonna do is detect sonorants or not  So sonorants and non - sonorants is  is  is almost like voiced - unvoiced , except I guess that the voiced stops are  are also called " obstruents ". Uh , so it 's  it 's  uh , but with the exception of the stops I guess it 's pretty much the same as voiced - unvoiced ,


So he hasn't applied it to recognition or if he did he didn't talk about it. It 's  it 's just  And one of the concerns in the audience , actually , was that  that , um , the , uh , uh  he  he did a comparison to , uh , you know , our old foil , the  the nasty old standard recognizer with  mel  mel filter bank at the front , and H M Ms , and  and so forth. And , um , it didn't do nearly as well , especially in  in noise. But the  one of the good questions in the audience was , well , yeah , but that wasn't trained for that. I mean , this use of a very smooth , uh , spectral envelope is something that , you know , has evolved as being generally a good thing for speech recognition but if you knew that what you were gonna do is detect sonorants or not  So sonorants and non - sonorants is  is  is almost like voiced - unvoiced , except I guess that the voiced stops are  are also called " obstruents ". Uh , so it 's  it 's  uh , but with the exception of the stops I guess it 's pretty much the same as voiced - unvoiced ,

right ? So  so  Um. So , um , if you knew you were doing that , if you were doing something say for a  a , uh  a  a Vocoder , you wouldn't use the same kind of features. You would use something that was sensitive to the periodicity and  and not just the envelope. Uh , and so in that sense it was an unfair test. Um , so I think that the questioner was right. It  it was in that sense an unfair test. Nonetheless , it was one that was interesting because , uh , this is what we are actually using for speech recognition , these smooth envelopes. And this says that perhaps even , you know , trying to use them in the best way that we can , that  that  that we ordinarily do , with , you know , Gaussian mixtures and H M Ms  and so forth , you  you don't , uh , actually do that well on determining whether something is sonorant or not.


right ? So  so  Um. So , um , if you knew you were doing that , if you were doing something say for a  a , uh  a  a Vocoder , you wouldn't use the same kind of features. You would use something that was sensitive to the periodicity and  and not just the envelope. Uh , and so in that sense it was an unfair test. Um , so I think that the questioner was right. It  it was in that sense an unfair test. Nonetheless , it was one that was interesting because , uh , this is what we are actually using for speech recognition , these smooth envelopes. And this says that perhaps even , you know , trying to use them in the best way that we can , that  that  that we ordinarily do , with , you know , Gaussian mixtures and H M Ms  and so forth , you  you don't , uh , actually do that well on determining whether something is sonorant or not.

Which means you 're gonna make errors between similar sounds that are son sonorant or obstruent.


right ? So  so  Um. So , um , if you knew you were doing that , if you were doing something say for a  a , uh  a  a Vocoder , you wouldn't use the same kind of features. You would use something that was sensitive to the periodicity and  and not just the envelope. Uh , and so in that sense it was an unfair test. Um , so I think that the questioner was right. It  it was in that sense an unfair test. Nonetheless , it was one that was interesting because , uh , this is what we are actually using for speech recognition , these smooth envelopes. And this says that perhaps even , you know , trying to use them in the best way that we can , that  that  that we ordinarily do , with , you know , Gaussian mixtures and H M Ms  and so forth , you  you don't , uh , actually do that well on determining whether something is sonorant or not.

Which means you 're gonna make errors between similar sounds that are son sonorant or obstruent.

Didn't they also do some kind of an oracle experiment where they said " if we  could detect the sonorants perfectly  and then show how it would improve speech recognition ? I thought I remember hearing about an experiment like that.


Which means you 're gonna make errors between similar sounds that are son sonorant or obstruent.

Didn't they also do some kind of an oracle experiment where they said " if we  could detect the sonorants perfectly  and then show how it would improve speech recognition ? I thought I remember hearing about an experiment like that.


Didn't they also do some kind of an oracle experiment where they said " if we  could detect the sonorants perfectly  and then show how it would improve speech recognition ? I thought I remember hearing about an experiment like that.


That would  that 's  you 're right , that 's exactly the question to follow up this discussion , is suppose you did that , uh , got that right. Um , Yeah.


That would  that 's  you 're right , that 's exactly the question to follow up this discussion , is suppose you did that , uh , got that right. Um , Yeah.

What could be the other low level detectors , I mean , for   Other kind of features , or  ? in addition to detecting sonorants or  ? Th - that 's what you want to  to  to go for also


What could be the other low level detectors , I mean , for   Other kind of features , or  ? in addition to detecting sonorants or  ? Th - that 's what you want to  to  to go for also


What could be the other low level detectors , I mean , for   Other kind of features , or  ? in addition to detecting sonorants or  ? Th - that 's what you want to  to  to go for also

What t Oh , build other  other detectors on different  phonetic features ?


What t Oh , build other  other detectors on different  phonetic features ?


What t Oh , build other  other detectors on different  phonetic features ?

Um , uh Let 's see , um , Yeah , I d I don't know. e Um , um , I mean , w easiest thing would be to go  go do some voicing stuff but that 's very similar to sonorance.


Um , uh Let 's see , um , Yeah , I d I don't know. e Um , um , I mean , w easiest thing would be to go  go do some voicing stuff but that 's very similar to sonorance.


Um , uh Let 's see , um , Yeah , I d I don't know. e Um , um , I mean , w easiest thing would be to go  go do some voicing stuff but that 's very similar to sonorance.

When we  when we talked with John Ohala the other day we made a list of some of the things that w


When we  when we talked with John Ohala the other day we made a list of some of the things that w


Yeah , so there 's a half dozen like that that are


Yeah , so there 's a half dozen like that that are

Now this was coming at it from a different angle but maybe it 's a good way to start. Uh , these are things which , uh , John felt that a  a , uh  a human annotator would be able to reliably mark. So the sort of things he felt would be difficult for a human annotator to reliably mark would be tongue position kinds of things.


Now this was coming at it from a different angle but maybe it 's a good way to start. Uh , these are things which , uh , John felt that a  a , uh  a human annotator would be able to reliably mark. So the sort of things he felt would be difficult for a human annotator to reliably mark would be tongue position kinds of things.


But stress doesn't , uh , fit in this thing of coming up with features that will distinguish words from one another ,


But stress doesn't , uh , fit in this thing of coming up with features that will distinguish words from one another ,

right ? It 's a  it 's a good thing to mark and will probably help us ultimate with recognition


But stress doesn't , uh , fit in this thing of coming up with features that will distinguish words from one another ,

right ? It 's a  it 's a good thing to mark and will probably help us ultimate with recognition

Yeah , there 's a few cases where it can like permit  and permit.


right ? It 's a  it 's a good thing to mark and will probably help us ultimate with recognition

Yeah , there 's a few cases where it can like permit  and permit.


right ? It 's a  it 's a good thing to mark and will probably help us ultimate with recognition

Yeah , there 's a few cases where it can like permit  and permit.

But  that 's not very common in English. In other languages it 's more uh , important.


Yeah , there 's a few cases where it can like permit  and permit.

But  that 's not very common in English. In other languages it 's more uh , important.

Well , yeah , but i either case you 'd write PERMIT , right ? So you 'd get the word right.


But  that 's not very common in English. In other languages it 's more uh , important.

Well , yeah , but i either case you 'd write PERMIT , right ? So you 'd get the word right.

No , I 'm saying , i i e I thought you were saying that stress doesn't help you distinguish between words.


Well , yeah , but i either case you 'd write PERMIT , right ? So you 'd get the word right.

No , I 'm saying , i i e I thought you were saying that stress doesn't help you distinguish between words.

Oh , I see what you 're saying. As long as you get  The sequence ,


No , I 'm saying , i i e I thought you were saying that stress doesn't help you distinguish between words.

Oh , I see what you 're saying. As long as you get  The sequence ,

We 're g if we 're doing  if we 're talking about transcription as opposed to something else


Oh , I see what you 're saying. As long as you get  The sequence ,

We 're g if we 're doing  if we 're talking about transcription as opposed to something else

right ? Yeah. Yeah , yeah , yeah. Yeah. Right.


We 're g if we 're doing  if we 're talking about transcription as opposed to something else

right ? Yeah. Yeah , yeah , yeah. Yeah. Right.

So where it could help is maybe at a higher level. Yeah.


right ? Yeah. Yeah , yeah , yeah. Yeah. Right.

So where it could help is maybe at a higher level. Yeah.


So where it could help is maybe at a higher level. Yeah.


But that 's this afternoon 's meeting. Yeah. We don't understand anything in this meeting. Yeah , so that 's  yeah , that 's , you know , a neat  neat thing and  and , uh  So.


But that 's this afternoon 's meeting. Yeah. We don't understand anything in this meeting. Yeah , so that 's  yeah , that 's , you know , a neat  neat thing and  and , uh  So.

S so , um , Ohala 's going to help do these , uh  transcriptions of the meeting data ?


But that 's this afternoon 's meeting. Yeah. We don't understand anything in this meeting. Yeah , so that 's  yeah , that 's , you know , a neat  neat thing and  and , uh  So.

S so , um , Ohala 's going to help do these , uh  transcriptions of the meeting data ?

Uh , well I don't know. We d we sort of didn't get that far. Um , we just talked about some possible features that could be marked by humans and , um ,


S so , um , Ohala 's going to help do these , uh  transcriptions of the meeting data ?

Uh , well I don't know. We d we sort of didn't get that far. Um , we just talked about some possible features that could be marked by humans and , um ,

because of having maybe some extra transcriber time we thought we could go through and mark some portion of the data for that. And , uh


Uh , well I don't know. We d we sort of didn't get that far. Um , we just talked about some possible features that could be marked by humans and , um ,

because of having maybe some extra transcriber time we thought we could go through and mark some portion of the data for that. And , uh


because of having maybe some extra transcriber time we thought we could go through and mark some portion of the data for that. And , uh


because of having maybe some extra transcriber time we thought we could go through and mark some portion of the data for that. And , uh

I mean , that 's not an immediate problem , that we don't immediately have a lot of extra transcriber time.


I mean , that 's not an immediate problem , that we don't immediately have a lot of extra transcriber time.


I mean , that 's not an immediate problem , that we don't immediately have a lot of extra transcriber time.

But  but , uh , in the long term I guess Chuck is gonna continue the dialogue with John and  and , uh , and , we 'll  we 'll end up doing some I think.


I mean , that 's not an immediate problem , that we don't immediately have a lot of extra transcriber time.

But  but , uh , in the long term I guess Chuck is gonna continue the dialogue with John and  and , uh , and , we 'll  we 'll end up doing some I think.

I 'm definitely interested in this area , too , f uh , acoustic feature stuff.


But  but , uh , in the long term I guess Chuck is gonna continue the dialogue with John and  and , uh , and , we 'll  we 'll end up doing some I think.

I 'm definitely interested in this area , too , f uh , acoustic feature stuff.


I 'm definitely interested in this area , too , f uh , acoustic feature stuff.


Yeah , I think it 's an interesting  interesting way to go.


Yeah , I think it 's an interesting  interesting way to go.

Um , I say it like " said - int ". I think it has a number of good things. Um , so , uh , y you want to talk maybe a c two or three minutes about what we 've been talking about today and other days ?


Yeah , I think it 's an interesting  interesting way to go.

Um , I say it like " said - int ". I think it has a number of good things. Um , so , uh , y you want to talk maybe a c two or three minutes about what we 've been talking about today and other days ?

Ri Yeah , OK , so , um , we 're interested in , um , methods for far mike speech recognition , um ,  mainly , uh , methods that deal with the reverberation  in the far mike signal. So , um , one approach would be , um , say MSG and PLP , like was used in Aurora one and , um , there are other approaches which actually attempt to  remove the reverberation , instead of being robust to it like MSG. And so we 're interested in , um , comparing the performance of  um , a robust approach like MSG with these , um , speech enhancement or de - reverber de - reverberation approaches.


Um , I say it like " said - int ". I think it has a number of good things. Um , so , uh , y you want to talk maybe a c two or three minutes about what we 've been talking about today and other days ?

Ri Yeah , OK , so , um , we 're interested in , um , methods for far mike speech recognition , um ,  mainly , uh , methods that deal with the reverberation  in the far mike signal. So , um , one approach would be , um , say MSG and PLP , like was used in Aurora one and , um , there are other approaches which actually attempt to  remove the reverberation , instead of being robust to it like MSG. And so we 're interested in , um , comparing the performance of  um , a robust approach like MSG with these , um , speech enhancement or de - reverber de - reverberation approaches.


Um , I say it like " said - int ". I think it has a number of good things. Um , so , uh , y you want to talk maybe a c two or three minutes about what we 've been talking about today and other days ?

Ri Yeah , OK , so , um , we 're interested in , um , methods for far mike speech recognition , um ,  mainly , uh , methods that deal with the reverberation  in the far mike signal. So , um , one approach would be , um , say MSG and PLP , like was used in Aurora one and , um , there are other approaches which actually attempt to  remove the reverberation , instead of being robust to it like MSG. And so we 're interested in , um , comparing the performance of  um , a robust approach like MSG with these , um , speech enhancement or de - reverber de - reverberation approaches.

And , um ,  it looks like we 're gonna use the Meeting Recorder digits data for that.


Ri Yeah , OK , so , um , we 're interested in , um , methods for far mike speech recognition , um ,  mainly , uh , methods that deal with the reverberation  in the far mike signal. So , um , one approach would be , um , say MSG and PLP , like was used in Aurora one and , um , there are other approaches which actually attempt to  remove the reverberation , instead of being robust to it like MSG. And so we 're interested in , um , comparing the performance of  um , a robust approach like MSG with these , um , speech enhancement or de - reverber de - reverberation approaches.

And , um ,  it looks like we 're gonna use the Meeting Recorder digits data for that.

And the de - reverberation algorithm , do you have  can you give some more details on this or  ? Does it use one microphone ?


And , um ,  it looks like we 're gonna use the Meeting Recorder digits data for that.

And the de - reverberation algorithm , do you have  can you give some more details on this or  ? Does it use one microphone ?


And the de - reverberation algorithm , do you have  can you give some more details on this or  ? Does it use one microphone ?

OK , well , um , there was something that was done by , um , a guy named Carlos , I forget his last name ,  who worked with Hynek , who , um ,


OK , well , um , there was something that was done by , um , a guy named Carlos , I forget his last name ,  who worked with Hynek , who , um ,


um , it was like RASTA in the sense that of it was , um , de - convolution by filtering um , except he used a longer time window ,


um , it was like RASTA in the sense that of it was , um , de - convolution by filtering um , except he used a longer time window ,

like a second maybe. And the reason for that is RASTA 's time window is too short to , um include the whole , um , reverberation  um , I don't know what you call it the reverberation response. I if you see wh if you see what I mean. The reverberation filter from my mouth to that mike is like  it 's t got it 's too long in the  in the time domain for the um  for the RASTA filtering to take care of it. And , um , then there are a couple of other speech enhancement approaches which haven't been tried for speech recognition yet but have just been tried for enhancement , which , um , have the assumption that um , you can do LPC um analysis of th of the signal you get at the far microphone and the , um , all pole filter that you get out of that should be good. It 's just the , um , excitation signal  that is going to be distorted by the reverberation and so you can try and reconstruct a better excitation signal and , um , feed that through the i um , all pole filter and get enhanced speech with reverberation reduced.


like a second maybe. And the reason for that is RASTA 's time window is too short to , um include the whole , um , reverberation  um , I don't know what you call it the reverberation response. I if you see wh if you see what I mean. The reverberation filter from my mouth to that mike is like  it 's t got it 's too long in the  in the time domain for the um  for the RASTA filtering to take care of it. And , um , then there are a couple of other speech enhancement approaches which haven't been tried for speech recognition yet but have just been tried for enhancement , which , um , have the assumption that um , you can do LPC um analysis of th of the signal you get at the far microphone and the , um , all pole filter that you get out of that should be good. It 's just the , um , excitation signal  that is going to be distorted by the reverberation and so you can try and reconstruct a better excitation signal and , um , feed that through the i um , all pole filter and get enhanced speech with reverberation reduced.

There 's also this , uh , um , uh , echo cancellation stuff that we 've sort of been chasing , so , uh we have , uh  and when we 're saying these digits now we do have a close microphone signal and then there 's the distant microphone signal. And you could as a kind of baseline say , " OK , given that we have both of these , uh , we should be able to do , uh , a cancellation. " So that , uh , um , we  we , uh , essentially identify the system in between  the linear time invariant system between the microphones and  and  and  and re and invert it , uh , or  or cancel it out to  to some  some reasonable approximation


There 's also this , uh , um , uh , echo cancellation stuff that we 've sort of been chasing , so , uh we have , uh  and when we 're saying these digits now we do have a close microphone signal and then there 's the distant microphone signal. And you could as a kind of baseline say , " OK , given that we have both of these , uh , we should be able to do , uh , a cancellation. " So that , uh , um , we  we , uh , essentially identify the system in between  the linear time invariant system between the microphones and  and  and  and re and invert it , uh , or  or cancel it out to  to some  some reasonable approximation

through one method or another. Uh , that 's not a practical thing , uh , if you have a distant mike , you don't have a close mike ordinarily , but we thought that might make  also might make a good baseline. Uh , it still won't be perfect because there 's noise. Uh , but  And then there are s uh , there are single microphone methods that I think people have done for , uh  for this kind of de - reverberation. Do y do you know any references to any ? Cuz I  I w I was  w w I  I lead him down a  a bad path on that.