But  Yeah. I don't know whether it monitors the keyboard or actually looks at the console TTY , so maybe if you echoed something to the you know , dev  dev console or something.

You probably wouldn't ordinarily , though. Yeah. Right ? You probably wouldn't ordinarily.

I mean you sort of  you 're at home and you 're trying to log in , and it takes forever to even log you in , and you probably go , " screw this " ,


You probably wouldn't ordinarily , though. Yeah. Right ? You probably wouldn't ordinarily.

I mean you sort of  you 're at home and you 're trying to log in , and it takes forever to even log you in , and you probably go , " screw this " ,


I mean you sort of  you 're at home and you 're trying to log in , and it takes forever to even log you in , and you probably go , " screw this " ,


yeah. I  I can  I 'm not sure about that one.


Uh , I need a little orientation about this environment and uh scr s how to run some jobs here because I never d did anything so far with this X emissions


Uh , I need a little orientation about this environment and uh scr s how to run some jobs here because I never d did anything so far with this X emissions

So , I think maybe I 'll ask you after the meeting.


Uh , I need a little orientation about this environment and uh scr s how to run some jobs here because I never d did anything so far with this X emissions

So , I think maybe I 'll ask you after the meeting.

Um. Yeah. Yeah , and  and also uh , Stephane 's a  a really good resource for that if you can't find me.


So , I think maybe I 'll ask you after the meeting.

Um. Yeah. Yeah , and  and also uh , Stephane 's a  a really good resource for that if you can't find me.


Um. Yeah. Yeah , and  and also uh , Stephane 's a  a really good resource for that if you can't find me.

Especially with regard to the Aurora stuff.


Especially with regard to the Aurora stuff.


Especially with regard to the Aurora stuff.

He  he knows that stuff better than I do.


Especially with regard to the Aurora stuff.

He  he knows that stuff better than I do.

OK. Well , why don't we uh , uh , Sunil since you 're  haven't  haven't been at one of these yet , why don't yo you tell us what 's  what 's up with you ? Wh - what you 've been up to , hopefully.


He  he knows that stuff better than I do.

OK. Well , why don't we uh , uh , Sunil since you 're  haven't  haven't been at one of these yet , why don't yo you tell us what 's  what 's up with you ? Wh - what you 've been up to , hopefully.

Um. Yeah. So , uh , shall I start from  Well I don't know how may I  how  OK. Uh , I think I 'll start from the post uh Aurora submission maybe.


OK. Well , why don't we uh , uh , Sunil since you 're  haven't  haven't been at one of these yet , why don't yo you tell us what 's  what 's up with you ? Wh - what you 've been up to , hopefully.

Um. Yeah. So , uh , shall I start from  Well I don't know how may I  how  OK. Uh , I think I 'll start from the post uh Aurora submission maybe.

Uh , yeah , after the submission the  what I 've been working on mainly was to take  take other s submissions and then over their system , what they submitted , because we didn't have any speech enhancement system in  in ours. So  So I tried uh , And u First I tried just LDA. And then I found that uh , I mean , if  if I combine it with LDA , it gives @ @ improvement over theirs. Uh


Um. Yeah. So , uh , shall I start from  Well I don't know how may I  how  OK. Uh , I think I 'll start from the post uh Aurora submission maybe.

Uh , yeah , after the submission the  what I 've been working on mainly was to take  take other s submissions and then over their system , what they submitted , because we didn't have any speech enhancement system in  in ours. So  So I tried uh , And u First I tried just LDA. And then I found that uh , I mean , if  if I combine it with LDA , it gives @ @ improvement over theirs. Uh


Uh , yeah , after the submission the  what I 've been working on mainly was to take  take other s submissions and then over their system , what they submitted , because we didn't have any speech enhancement system in  in ours. So  So I tried uh , And u First I tried just LDA. And then I found that uh , I mean , if  if I combine it with LDA , it gives @ @ improvement over theirs. Uh


So , just  just the LDA filters. I just plug in  I just take the cepstral coefficients coming from their system and then plug in LDA on top of that. But the LDA filter that I used was different from what we submitted in the proposal.


So , just  just the LDA filters. I just plug in  I just take the cepstral coefficients coming from their system and then plug in LDA on top of that. But the LDA filter that I used was different from what we submitted in the proposal.

What I did was  I took the LDA filter 's design using clean speech , uh , mainly because the speech is already cleaned up after the enhancement so , instead of using this , uh , narrow  narrow band LDA filter that we submitted uh , I got new filters. So that seems to be giving  uh , improving over their uh , system. Slightly. But , not very significantly. And uh , that was uh , showing any improvement over  final  by plugging in an LDA. And uh , so then after  after that I  I added uh , on - line normalization also on top of that. And that  there  there also I n I found that I have to make some changes to their time constant that I used because th it has a  a mean and variance update time constant and  which is not suitable for the enhanced speech , and whatever we try it on with proposal - one. But um , I didn't  I didn't play with that time constant a lot , I just t g I just found that I have to reduce the value  I mean , I have to increase the time constant , or reduce the value of the update value. That 's all I found So I have to. Uh , Yeah. And uh , uh , the other  other thing what I tried was , I just um , uh , took the baseline and then ran it with the endpoint inf uh th information , just the Aurora baseline , to see that how much the baseline itself improves by just supplying the information of the  I mean the w speech and nonspeech. And uh , I found that the baseline itself improves by twenty - two percent by just giving the wuh.


So , just  just the LDA filters. I just plug in  I just take the cepstral coefficients coming from their system and then plug in LDA on top of that. But the LDA filter that I used was different from what we submitted in the proposal.

What I did was  I took the LDA filter 's design using clean speech , uh , mainly because the speech is already cleaned up after the enhancement so , instead of using this , uh , narrow  narrow band LDA filter that we submitted uh , I got new filters. So that seems to be giving  uh , improving over their uh , system. Slightly. But , not very significantly. And uh , that was uh , showing any improvement over  final  by plugging in an LDA. And uh , so then after  after that I  I added uh , on - line normalization also on top of that. And that  there  there also I n I found that I have to make some changes to their time constant that I used because th it has a  a mean and variance update time constant and  which is not suitable for the enhanced speech , and whatever we try it on with proposal - one. But um , I didn't  I didn't play with that time constant a lot , I just t g I just found that I have to reduce the value  I mean , I have to increase the time constant , or reduce the value of the update value. That 's all I found So I have to. Uh , Yeah. And uh , uh , the other  other thing what I tried was , I just um , uh , took the baseline and then ran it with the endpoint inf uh th information , just the Aurora baseline , to see that how much the baseline itself improves by just supplying the information of the  I mean the w speech and nonspeech. And uh , I found that the baseline itself improves by twenty - two percent by just giving the wuh.

Uh , can you back up a second , I  I  I missed something , uh , I guess my mind wandered. Ad - ad When you added the on - line normalization and so forth , uh , uh things got better again ?


What I did was  I took the LDA filter 's design using clean speech , uh , mainly because the speech is already cleaned up after the enhancement so , instead of using this , uh , narrow  narrow band LDA filter that we submitted uh , I got new filters. So that seems to be giving  uh , improving over their uh , system. Slightly. But , not very significantly. And uh , that was uh , showing any improvement over  final  by plugging in an LDA. And uh , so then after  after that I  I added uh , on - line normalization also on top of that. And that  there  there also I n I found that I have to make some changes to their time constant that I used because th it has a  a mean and variance update time constant and  which is not suitable for the enhanced speech , and whatever we try it on with proposal - one. But um , I didn't  I didn't play with that time constant a lot , I just t g I just found that I have to reduce the value  I mean , I have to increase the time constant , or reduce the value of the update value. That 's all I found So I have to. Uh , Yeah. And uh , uh , the other  other thing what I tried was , I just um , uh , took the baseline and then ran it with the endpoint inf uh th information , just the Aurora baseline , to see that how much the baseline itself improves by just supplying the information of the  I mean the w speech and nonspeech. And uh , I found that the baseline itself improves by twenty - two percent by just giving the wuh.

Uh , can you back up a second , I  I  I missed something , uh , I guess my mind wandered. Ad - ad When you added the on - line normalization and so forth , uh , uh things got better again ?


Uh , can you back up a second , I  I  I missed something , uh , I guess my mind wandered. Ad - ad When you added the on - line normalization and so forth , uh , uh things got better again ?

No. No , things didn't get better with the same time constant that we used.


No. No , things didn't get better with the same time constant that we used.

Did it not ? No , no. With a different time constant.


No. No , things didn't get better with the same time constant that we used.

Did it not ? No , no. With a different time constant.

With the different time constant I found that  I mean , I didn't get an improvement over not using on - line normalization ,


Did it not ? No , no. With a different time constant.

With the different time constant I found that  I mean , I didn't get an improvement over not using on - line normalization ,

because I  I found that I would have change the value of the update factor.


With the different time constant I found that  I mean , I didn't get an improvement over not using on - line normalization ,

because I  I found that I would have change the value of the update factor.


because I  I found that I would have change the value of the update factor.

But I didn't play it with play  play quite a bit to make it better than.


But I didn't play it with play  play quite a bit to make it better than.


I mean , the on - line normalization didn't give me any improvement.


oh yeah So I just stopped there with the uh , speech enhancement. The  the other thing what I tried was the  adding the uh , endpoint information to the baseline and that itself gives like twenty - two percent because the  the second  the new phase is going to be with the endpointed speech. And just to get a feel of how much the baseline itself is going to change by adding this endpoint information , I just , uh , use


oh yeah So I just stopped there with the uh , speech enhancement. The  the other thing what I tried was the  adding the uh , endpoint information to the baseline and that itself gives like twenty - two percent because the  the second  the new phase is going to be with the endpointed speech. And just to get a feel of how much the baseline itself is going to change by adding this endpoint information , I just , uh , use

So people won't even have to worry about , uh , doing speech - nonspeech then.


oh yeah So I just stopped there with the uh , speech enhancement. The  the other thing what I tried was the  adding the uh , endpoint information to the baseline and that itself gives like twenty - two percent because the  the second  the new phase is going to be with the endpointed speech. And just to get a feel of how much the baseline itself is going to change by adding this endpoint information , I just , uh , use

So people won't even have to worry about , uh , doing speech - nonspeech then.

Yeah that 's , that 's what the feeling is like. They 're going to give the endpoint information.


So people won't even have to worry about , uh , doing speech - nonspeech then.

Yeah that 's , that 's what the feeling is like. They 're going to give the endpoint information.


So people won't even have to worry about , uh , doing speech - nonspeech then.

Yeah that 's , that 's what the feeling is like. They 're going to give the endpoint information.

G I guess the issue is that people do that anyway ,


Yeah that 's , that 's what the feeling is like. They 're going to give the endpoint information.

G I guess the issue is that people do that anyway ,


G I guess the issue is that people do that anyway ,


and they wanted to see , given that you 're doing that , what  what are the best features that you should use.


and they wanted to see , given that you 're doing that , what  what are the best features that you should use.

I mean clearly they 're interact. So I don't know that I entirely agree with it.


I mean clearly they 're interact. So I don't know that I entirely agree with it.


I mean clearly they 're interact. So I don't know that I entirely agree with it.

But  but it might be uh  In some ways it might be better t to  rather than giving the endpoints , to have a standard that everybody uses and then interacts with.


But  but it might be uh  In some ways it might be better t to  rather than giving the endpoints , to have a standard that everybody uses and then interacts with.

But , you know. It 's  it 's still someth reasonable.


But  but it might be uh  In some ways it might be better t to  rather than giving the endpoints , to have a standard that everybody uses and then interacts with.

But , you know. It 's  it 's still someth reasonable.

So , are people supposed to assume that there is uh  Are  are people not supposed to use any speech outside of those endpoints ?


But , you know. It 's  it 's still someth reasonable.

So , are people supposed to assume that there is uh  Are  are people not supposed to use any speech outside of those endpoints ?


But , you know. It 's  it 's still someth reasonable.

So , are people supposed to assume that there is uh  Are  are people not supposed to use any speech outside of those endpoints ?

Or can you then use speech outside of it for estimating background noise and things ?


So , are people supposed to assume that there is uh  Are  are people not supposed to use any speech outside of those endpoints ?

Or can you then use speech outside of it for estimating background noise and things ?

No. No. That i I  Yeah. Yeah , yeah , exactly. I guess that is  that is where the consensus is. Like y you will  you will  You 'll be given the information about the beginning and the end of speech but the whole speech is available to you.


Or can you then use speech outside of it for estimating background noise and things ?

No. No. That i I  Yeah. Yeah , yeah , exactly. I guess that is  that is where the consensus is. Like y you will  you will  You 'll be given the information about the beginning and the end of speech but the whole speech is available to you.


No. No. That i I  Yeah. Yeah , yeah , exactly. I guess that is  that is where the consensus is. Like y you will  you will  You 'll be given the information about the beginning and the end of speech but the whole speech is available to you.

So it should make the spectral subtraction style things work even better ,


So it should make the spectral subtraction style things work even better ,


So it should make the spectral subtraction style things work even better ,

because you don't have the mistakes in it. Yeah ?


because you don't have the mistakes in it. Yeah ?


because you don't have the mistakes in it. Yeah ?

So that  that  The baseline itself  I mean , it improves by twenty - two percent. I found that in s one of the SpeechDat - Car cases , that like , the Spanish one improves by just fifty percent by just putting the endpoint. w


So that  that  The baseline itself  I mean , it improves by twenty - two percent. I found that in s one of the SpeechDat - Car cases , that like , the Spanish one improves by just fifty percent by just putting the endpoint. w


So that  that  The baseline itself  I mean , it improves by twenty - two percent. I found that in s one of the SpeechDat - Car cases , that like , the Spanish one improves by just fifty percent by just putting the endpoint. w

I mean you don't need any further speech enhancement with fifty. So , uh ,


So that  that  The baseline itself  I mean , it improves by twenty - two percent. I found that in s one of the SpeechDat - Car cases , that like , the Spanish one improves by just fifty percent by just putting the endpoint. w

I mean you don't need any further speech enhancement with fifty. So , uh ,

So the baseline itself improves by fifty percent.


I mean you don't need any further speech enhancement with fifty. So , uh ,

So the baseline itself improves by fifty percent.


So the baseline itself improves by fifty percent.


So it 's g it 's gonna be harder to  beat that actually.


so that is when uh , the  the qualification criteria was reduced from fifty percent to something like twenty - five percent for well - matched. And I think they have  they have actually changed their qualification c criteria now. And uh , Yeah , I guess after that , I just went home f I just had a vacation fo for four weeks. Uh.


so that is when uh , the  the qualification criteria was reduced from fifty percent to something like twenty - five percent for well - matched. And I think they have  they have actually changed their qualification c criteria now. And uh , Yeah , I guess after that , I just went home f I just had a vacation fo for four weeks. Uh.

OK. No , that 's  that 's  that 's a good  good update.


so that is when uh , the  the qualification criteria was reduced from fifty percent to something like twenty - five percent for well - matched. And I think they have  they have actually changed their qualification c criteria now. And uh , Yeah , I guess after that , I just went home f I just had a vacation fo for four weeks. Uh.

OK. No , that 's  that 's  that 's a good  good update.

Ye Yeah , and I  I came back and I started working on uh , some other speech enhancement algorithm. I mean , so  I  from the submission what I found that people have tried spectral subtraction and Wiener filtering. These are the main uh , approaches where people have tried ,


OK. No , that 's  that 's  that 's a good  good update.

Ye Yeah , and I  I came back and I started working on uh , some other speech enhancement algorithm. I mean , so  I  from the submission what I found that people have tried spectral subtraction and Wiener filtering. These are the main uh , approaches where people have tried ,

so just to  just to fill the space with some f few more speech enhancement algorithms to see whether it improves a lot , I  I 've been working on this uh , signal subspace approach for speech enhancement where you take the noisy signal and then decomposing the signal s and the noise subspace and then try to estimate the clean speech from the signal plus noise subspace. And


Ye Yeah , and I  I came back and I started working on uh , some other speech enhancement algorithm. I mean , so  I  from the submission what I found that people have tried spectral subtraction and Wiener filtering. These are the main uh , approaches where people have tried ,

so just to  just to fill the space with some f few more speech enhancement algorithms to see whether it improves a lot , I  I 've been working on this uh , signal subspace approach for speech enhancement where you take the noisy signal and then decomposing the signal s and the noise subspace and then try to estimate the clean speech from the signal plus noise subspace. And


so just to  just to fill the space with some f few more speech enhancement algorithms to see whether it improves a lot , I  I 've been working on this uh , signal subspace approach for speech enhancement where you take the noisy signal and then decomposing the signal s and the noise subspace and then try to estimate the clean speech from the signal plus noise subspace. And

So , I 've been actually running some s So far I 've been trying it only on Matlab. I have to  to  to test whether it works first or not


So , I 've been actually running some s So far I 've been trying it only on Matlab. I have to  to  to test whether it works first or not

and then I 'll p port it to C and I 'll update it with the repository once I find it it giving any some positive result. So , yeah.


So , I 've been actually running some s So far I 've been trying it only on Matlab. I have to  to  to test whether it works first or not

and then I 'll p port it to C and I 'll update it with the repository once I find it it giving any some positive result. So , yeah.

S So you s you So you said one thing I want to jump on for a second. So  so now you 're  you 're getting tuned into the repository thing that he has here


and then I 'll p port it to C and I 'll update it with the repository once I find it it giving any some positive result. So , yeah.

S So you s you So you said one thing I want to jump on for a second. So  so now you 're  you 're getting tuned into the repository thing that he has here


and then I 'll p port it to C and I 'll update it with the repository once I find it it giving any some positive result. So , yeah.

S So you s you So you said one thing I want to jump on for a second. So  so now you 're  you 're getting tuned into the repository thing that he has here

and  so we we 'll have a  single place where the stuff is.


S So you s you So you said one thing I want to jump on for a second. So  so now you 're  you 're getting tuned into the repository thing that he has here

and  so we we 'll have a  single place where the stuff is.


and  so we we 'll have a  single place where the stuff is.

Cool. Um , so maybe uh , just briefly , you could remind us about the related experiments. Cuz you did some stuff that you talked about last week , I guess ?


Cool. Um , so maybe uh , just briefly , you could remind us about the related experiments. Cuz you did some stuff that you talked about last week , I guess ?

Um , where you were also combining something  both of you I guess were both combining something from the uh , French Telecom system with  the u uh


Um , where you were also combining something  both of you I guess were both combining something from the uh , French Telecom system with  the u uh

I  I don't know whether it was system one or system two , or  ?


I  I don't know whether it was system one or system two , or  ?


I  I don't know whether it was system one or system two , or  ?

we  The main thing that we did is just to take the spectral subtraction from the France Telecom , which provide us some speech samples that are uh , with noise removed.


we  The main thing that we did is just to take the spectral subtraction from the France Telecom , which provide us some speech samples that are uh , with noise removed.

So I let me  let me just stop you there. So then , one distinction is that uh , you were taking the actual France Telecom features and then applying something to


we  The main thing that we did is just to take the spectral subtraction from the France Telecom , which provide us some speech samples that are uh , with noise removed.

So I let me  let me just stop you there. So then , one distinction is that uh , you were taking the actual France Telecom features and then applying something to

Uh , no there is a slight different. Uh I mean , which are extracted at the handset because they had another back - end blind equalization


So I let me  let me just stop you there. So then , one distinction is that uh , you were taking the actual France Telecom features and then applying something to

Uh , no there is a slight different. Uh I mean , which are extracted at the handset because they had another back - end blind equalization


Uh , no there is a slight different. Uh I mean , which are extracted at the handset because they had another back - end blind equalization


yeah , I 'm not being  I 'm not being clear.


yeah , I 'm not being  I 'm not being clear.

What I meant was you had something like cepstra or something , right ?


What I meant was you had something like cepstra or something , right ?

And so one difference is that , I guess you were taking spectra.


And so one difference is that , I guess you were taking spectra.


And so one difference is that , I guess you were taking spectra.

Yeah. But I guess it 's the s exactly the same thing because on the heads uh , handset they just applied this Wiener filter and then compute cepstral features ,


Yeah. But I guess it 's the s exactly the same thing because on the heads uh , handset they just applied this Wiener filter and then compute cepstral features ,

Yeah , the cepstral f The difference is like  There may be a slight difference in the way


Yeah. But I guess it 's the s exactly the same thing because on the heads uh , handset they just applied this Wiener filter and then compute cepstral features ,

Yeah , the cepstral f The difference is like  There may be a slight difference in the way

because they use exactly the baseline system for converting the cepstrum once you have the speech. I mean , if we are using our own code for th I mean that  that could be the only difference.


Yeah , the cepstral f The difference is like  There may be a slight difference in the way

because they use exactly the baseline system for converting the cepstrum once you have the speech. I mean , if we are using our own code for th I mean that  that could be the only difference.


because they use exactly the baseline system for converting the cepstrum once you have the speech. I mean , if we are using our own code for th I mean that  that could be the only difference.


But you got some sort of different result. So I 'm trying to understand it. But uh , I th


But you got some sort of different result. So I 'm trying to understand it. But uh , I th

Yeah , well I think we should uh , have a table with all the result because I don't know I uh , I don't exactly know what are your results ? But ,


But you got some sort of different result. So I 'm trying to understand it. But uh , I th

Yeah , well I think we should uh , have a table with all the result because I don't know I uh , I don't exactly know what are your results ? But ,

Mmm. Yeah , but so we did this , and another difference I guess is that we just applied uh , proposal - one system after this without  well , with our modification to reduce the delay of the  the LDA filters ,


Yeah , well I think we should uh , have a table with all the result because I don't know I uh , I don't exactly know what are your results ? But ,

Mmm. Yeah , but so we did this , and another difference I guess is that we just applied uh , proposal - one system after this without  well , with our modification to reduce the delay of the  the LDA filters ,


Mmm. Yeah , but so we did this , and another difference I guess is that we just applied uh , proposal - one system after this without  well , with our modification to reduce the delay of the  the LDA filters ,


Well there are slight modifications , but it was the full proposal - one. In your case , if you tried just putting LDA , then maybe on - line normalization  ?


Well there are slight modifications , but it was the full proposal - one. In your case , if you tried just putting LDA , then maybe on - line normalization  ?

Only LDA. Yeah. Af - I  after that I added on - line normalization , yeah.


Well there are slight modifications , but it was the full proposal - one. In your case , if you tried just putting LDA , then maybe on - line normalization  ?

Only LDA. Yeah. Af - I  after that I added on - line normalization , yeah.

Mm - hmm. So we just tried directly to  to just , keep the system as it was and , um , when we plug the spectral subtraction it improves uh , signif significantly. Um , but , what seems clear also is that we have to retune the time constants of the on - line normalization.


Only LDA. Yeah. Af - I  after that I added on - line normalization , yeah.

Mm - hmm. So we just tried directly to  to just , keep the system as it was and , um , when we plug the spectral subtraction it improves uh , signif significantly. Um , but , what seems clear also is that we have to retune the time constants of the on - line normalization.

Because if we keep the value that was submitted uh , it doesn't help at all. You can remove on - line normalization , or put it , it doesn't change anything. Uh , uh , as long as you have the spectral subtraction. But , you can still find some kind of optimum somewhere , and we don't know where exactly


Mm - hmm. So we just tried directly to  to just , keep the system as it was and , um , when we plug the spectral subtraction it improves uh , signif significantly. Um , but , what seems clear also is that we have to retune the time constants of the on - line normalization.

Because if we keep the value that was submitted uh , it doesn't help at all. You can remove on - line normalization , or put it , it doesn't change anything. Uh , uh , as long as you have the spectral subtraction. But , you can still find some kind of optimum somewhere , and we don't know where exactly


Because if we keep the value that was submitted uh , it doesn't help at all. You can remove on - line normalization , or put it , it doesn't change anything. Uh , uh , as long as you have the spectral subtraction. But , you can still find some kind of optimum somewhere , and we don't know where exactly


So it sounds like you should look at some tables of results or something


and see where i where the   where they were different and what we can learn from it.


Oh yeah , I mean the  the new LDA filters.


Oh yeah , I mean the  the new LDA filters.

Yeah. LDA filters. There are other things that we finally were shown to improve also like , the sixty - four hertz cut - off.


Yeah. LDA filters. There are other things that we finally were shown to improve also like , the sixty - four hertz cut - off.


Yeah. LDA filters. There are other things that we finally were shown to improve also like , the sixty - four hertz cut - off.

w Uh , it doesn't seem to hurt on TI - digits , finally.


w Uh , it doesn't seem to hurt on TI - digits , finally.


Um , well there are some  minor changes , yeah.


Um , well there are some  minor changes , yeah.

And , right now if we look at the results , it 's , um , always better than  it seems always better than France Telecom for mismatch and high - mismatch. And it 's still slightly worse for well - matched.


And , right now if we look at the results , it 's , um , always better than  it seems always better than France Telecom for mismatch and high - mismatch. And it 's still slightly worse for well - matched.

Um , but this is not significant. But , the problem is that it 's not significant , but if you put this in the , mmm , uh , spreadsheet , it 's still worse. Even with very minor  uh , even if it 's only slightly worse for well - matched.


Um , but this is not significant. But , the problem is that it 's not significant , but if you put this in the , mmm , uh , spreadsheet , it 's still worse. Even with very minor  uh , even if it 's only slightly worse for well - matched.

And significantly better for HM. Uh , but , well. I don't think it 's importa important because when they will change their metric , uh , uh , mainly because of uh , when you p you plug the um , frame dropping in the baseline system , it will improve a lot HM , and MM ,


And significantly better for HM. Uh , but , well. I don't think it 's importa important because when they will change their metric , uh , uh , mainly because of uh , when you p you plug the um , frame dropping in the baseline system , it will improve a lot HM , and MM ,

so , um , I guess what will happen  I don't know what will happen. But , the different contribution , I think , for the different test set will be more even.


And significantly better for HM. Uh , but , well. I don't think it 's importa important because when they will change their metric , uh , uh , mainly because of uh , when you p you plug the um , frame dropping in the baseline system , it will improve a lot HM , and MM ,

so , um , I guess what will happen  I don't know what will happen. But , the different contribution , I think , for the different test set will be more even.

Because the  your improvement on HM and MM will also go down significantly in the spreadsheet so. But the  the well - matched may still


so , um , I guess what will happen  I don't know what will happen. But , the different contribution , I think , for the different test set will be more even.

Because the  your improvement on HM and MM will also go down significantly in the spreadsheet so. But the  the well - matched may still


so , um , I guess what will happen  I don't know what will happen. But , the different contribution , I think , for the different test set will be more even.

Because the  your improvement on HM and MM will also go down significantly in the spreadsheet so. But the  the well - matched may still

I mean the well - matched may be the one which is least affected by adding the endpoint information.


Because the  your improvement on HM and MM will also go down significantly in the spreadsheet so. But the  the well - matched may still

I mean the well - matched may be the one which is least affected by adding the endpoint information.


I mean the well - matched may be the one which is least affected by adding the endpoint information.


MM and HM are going to be v hugely affected by it. Yeah.


MM and HM are going to be v hugely affected by it. Yeah.

Yeah. But they d the  everything I mean is like , but there that 's how they reduce  why they reduce the qualification to twenty - five percent or some  something on.


Yeah. But they d the  everything I mean is like , but there that 's how they reduce  why they reduce the qualification to twenty - five percent or some  something on.


Yeah. But they d the  everything I mean is like , but there that 's how they reduce  why they reduce the qualification to twenty - five percent or some  something on.

Uh , no , I guess they are going ahead with the same weighting.


Uh , no , I guess they are going ahead with the same weighting.


I guess I  I haven't been part of the discussion , so , um , it seems to me that the well - matched condition is gonna be unusual ,


Because , um , you don't actually have good matches ordinarily for what any @ @  particular person 's car is like , or


It seems like something like the middle one is  is more natural.


It seems like something like the middle one is  is more natural.

So I don't know why the  well - matched is uh


So I don't know why the  well - matched is uh

Yeah , but actually the well  well the well - matched um , uh , I mean the  the well - matched condition is not like , uh , the one in TI - digits where uh , you have all the training , uh , conditions exactly like replicated in the testing condition also. It 's like , this is not calibrated by SNR or something. The well - matched has also some  some mismatch in that which is other than the


Yeah , but actually the well  well the well - matched um , uh , I mean the  the well - matched condition is not like , uh , the one in TI - digits where uh , you have all the training , uh , conditions exactly like replicated in the testing condition also. It 's like , this is not calibrated by SNR or something. The well - matched has also some  some mismatch in that which is other than the

has  has also some slight mismatches , unlike the TI - digits where it 's like prefectly matched


has  has also some slight mismatches , unlike the TI - digits where it 's like prefectly matched


Yeah. So remind me of what well - matched meant ?


Yeah. So remind me of what well - matched meant ?

the  the well - matched is defined like it 's seventy percent of the whole database is used for training and thirty percent for testing.


the  the well - matched is defined like it 's seventy percent of the whole database is used for training and thirty percent for testing.

Yeah. Well , so it means that if the database is large enough , it 's matched.


Yeah. Well , so it means that if the database is large enough , it 's matched.


in each set you have a range of conditions  Well


in each set you have a range of conditions  Well

Right. So , I mean , yeah , unless they deliberately chose it to be different , which they didn't because they want it to be well - matched , it is pretty much  You know , so it 's  so it 's sort of saying if you


Right. So , I mean , yeah , unless they deliberately chose it to be different , which they didn't because they want it to be well - matched , it is pretty much  You know , so it 's  so it 's sort of saying if you


Yeah because the m the main  major reason for the m


Yeah because the m the main  major reason for the m

the main mismatch is coming from the amount of noise and the silence frames and all those present in the database actually.


Yeah because the m the main  major reason for the m

the main mismatch is coming from the amount of noise and the silence frames and all those present in the database actually.

Again , if you have enough  if you have enough


the main mismatch is coming from the amount of noise and the silence frames and all those present in the database actually.

Again , if you have enough  if you have enough


the main mismatch is coming from the amount of noise and the silence frames and all those present in the database actually.

Again , if you have enough  if you have enough

So it 's sort of i i it 's sort of saying OK , so you  much as you train your dictation machine for talking into your computer , um , you  you have a car , and so you drive it around a bunch and  and record noise conditions , or something , and then  I don't think that 's very realistic , I mean I th


Again , if you have enough  if you have enough

So it 's sort of i i it 's sort of saying OK , so you  much as you train your dictation machine for talking into your computer , um , you  you have a car , and so you drive it around a bunch and  and record noise conditions , or something , and then  I don't think that 's very realistic , I mean I th


So it 's sort of i i it 's sort of saying OK , so you  much as you train your dictation machine for talking into your computer , um , you  you have a car , and so you drive it around a bunch and  and record noise conditions , or something , and then  I don't think that 's very realistic , I mean I th

I  I you know , so I  I  I  you know , I guess they 're saying that if you were a company that was selling the stuff commercially , that you would have a bunch of people driving around in a bunch of cars , and  and you would have something that was roughly similar and maybe that 's the argument , but I 'm not sure I buy it , so.


I  I you know , so I  I  I  you know , I guess they 're saying that if you were a company that was selling the stuff commercially , that you would have a bunch of people driving around in a bunch of cars , and  and you would have something that was roughly similar and maybe that 's the argument , but I 'm not sure I buy it , so.


I  I you know , so I  I  I  you know , I guess they 're saying that if you were a company that was selling the stuff commercially , that you would have a bunch of people driving around in a bunch of cars , and  and you would have something that was roughly similar and maybe that 's the argument , but I 'm not sure I buy it , so.

Mmm. You Yeah. We are playing  we are also playing , trying to put other spectral subtraction mmm , in the code. Um , it would be a very simple spectral subtraction , on the um , mel energies which I already tested but without the um frame dropping actually , and I think it 's important to have frame dropping if you use spectral subtraction.


Mmm. You Yeah. We are playing  we are also playing , trying to put other spectral subtraction mmm , in the code. Um , it would be a very simple spectral subtraction , on the um , mel energies which I already tested but without the um frame dropping actually , and I think it 's important to have frame dropping if you use spectral subtraction.

Is it  is spectral subtraction typically done on the  after the mel , uh , scaling or is it done on the FFT bins ?


Is it  is spectral subtraction typically done on the  after the mel , uh , scaling or is it done on the FFT bins ?

I d I don't know. Well , it 's both  both uh , cases can i


I d I don't know. Well , it 's both  both uh , cases can i


I d I don't know. Well , it 's both  both uh , cases can i

Yeah. So - some of the proposal , uh , we 're doing this on the bin  on the FFT bins ,


Yeah. So - some of the proposal , uh , we 're doing this on the bin  on the FFT bins ,

others on the um , mel energies. You can do both , but I cannot tell you what 's  which one might be better or  I


others on the um , mel energies. You can do both , but I cannot tell you what 's  which one might be better or  I

I guess if you want to reconstruct the speech , it may be a good idea to do it on FFT bins.


I guess if you want to reconstruct the speech , it may be a good idea to do it on FFT bins.


I guess if you want to reconstruct the speech , it may be a good idea to do it on FFT bins.

But for speech recognition , it may not. I mean it may not be very different if you do it on mel warped or whether you do it on FFT. So you 're going to do a linear weighting anyway after that.


But for speech recognition , it may not. I mean it may not be very different if you do it on mel warped or whether you do it on FFT. So you 're going to do a linear weighting anyway after that.


So , it may not be really a big different.


So , it may not be really a big different.

Well , it gives something different , but I don't know what are the , pros and cons of both.


Well , it gives something different , but I don't know what are the , pros and cons of both.


The other thing is like when you 're putting in a speech enhancement technique , uh , is it like one stage speech enhancement ? Because everybody seems to have a mod two stages of speech enhancement in all the proposals , which is really giving them some improvement.


I mean they just do the same thing again once more.


I mean they just do the same thing again once more.

And  So , there 's something that is good about doing it  I mean , to cleaning it up once more.


And  So , there 's something that is good about doing it  I mean , to cleaning it up once more.


So maybe in my implementation I should also try to inspire me from this kind of thing


So maybe in my implementation I should also try to inspire me from this kind of thing

Well , the other thing would be to combine what you 're doing.


Well , the other thing would be to combine what you 're doing.

I mean maybe one or  one or the other of the things that you 're doing would benefit from the other happening first.


I mean maybe one or  one or the other of the things that you 're doing would benefit from the other happening first.

Right , so he 's doing a signal subspace thing , maybe it would work better if you 'd already done some simple spectral subtraction , or maybe vi maybe the other way around ,


Right , so he 's doing a signal subspace thing , maybe it would work better if you 'd already done some simple spectral subtraction , or maybe vi maybe the other way around ,


So I 've been thinking about combining the Wiener filtering with signal subspace ,


So I 've been thinking about combining the Wiener filtering with signal subspace ,

I mean just to see all  some  some such permutation combination to see whether it really helps or not.


So I 've been thinking about combining the Wiener filtering with signal subspace ,

I mean just to see all  some  some such permutation combination to see whether it really helps or not.

Mm - hmm. Mm - hmm. Mm - hmm. Yeah. Yeah.


I mean just to see all  some  some such permutation combination to see whether it really helps or not.

Mm - hmm. Mm - hmm. Mm - hmm. Yeah. Yeah.

How is it  I  I guess I 'm ignorant about this , how does  I mean , since Wiener filter also assumes that you 're  that you 're adding together the two signals , how is  how is that differ from signal subspace ?


Mm - hmm. Mm - hmm. Mm - hmm. Yeah. Yeah.

How is it  I  I guess I 'm ignorant about this , how does  I mean , since Wiener filter also assumes that you 're  that you 're adding together the two signals , how is  how is that differ from signal subspace ?


How is it  I  I guess I 'm ignorant about this , how does  I mean , since Wiener filter also assumes that you 're  that you 're adding together the two signals , how is  how is that differ from signal subspace ?

The signal subspace approach has actually an in - built Wiener filtering in it.


The signal subspace approach has actually an in - built Wiener filtering in it.


The signal subspace approach has actually an in - built Wiener filtering in it.

Yeah. It is like a KL transform followed by a Wiener filter. Is the signal is  is a signal substrate.


The signal subspace approach has actually an in - built Wiener filtering in it.

Yeah. It is like a KL transform followed by a Wiener filter. Is the signal is  is a signal substrate.

Oh , oh , OK so the difference is the KL.


Yeah. It is like a KL transform followed by a Wiener filter. Is the signal is  is a signal substrate.

Oh , oh , OK so the difference is the KL.

So , the  the different  the c the  the advantage of combining two things is mainly coming from the signal subspace approach doesn't work very well if the SNR is very bad. It 's  it works very poorly with the poor SNR conditions , and in colored noise.


Yeah. It is like a KL transform followed by a Wiener filter. Is the signal is  is a signal substrate.

Oh , oh , OK so the difference is the KL.

So , the  the different  the c the  the advantage of combining two things is mainly coming from the signal subspace approach doesn't work very well if the SNR is very bad. It 's  it works very poorly with the poor SNR conditions , and in colored noise.

I see. So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a


Oh , oh , OK so the difference is the KL.

So , the  the different  the c the  the advantage of combining two things is mainly coming from the signal subspace approach doesn't work very well if the SNR is very bad. It 's  it works very poorly with the poor SNR conditions , and in colored noise.

I see. So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a

Wiener filtering. It 's a  it 's a cascade of two s


So , the  the different  the c the  the advantage of combining two things is mainly coming from the signal subspace approach doesn't work very well if the SNR is very bad. It 's  it works very poorly with the poor SNR conditions , and in colored noise.

I see. So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a

Wiener filtering. It 's a  it 's a cascade of two s

Wiener filter. Yeah , in general , you don't  that 's right you don't wanna othorg orthogonalize if the things are noisy. Actually. Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff , that if you 're converting things to from uh , bands , groups of bands into cepstral coef you know , local sort of local cepstral coefficients that it 's not that great to do it if it 's noisy.


I see. So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a

Wiener filtering. It 's a  it 's a cascade of two s

Wiener filter. Yeah , in general , you don't  that 's right you don't wanna othorg orthogonalize if the things are noisy. Actually. Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff , that if you 're converting things to from uh , bands , groups of bands into cepstral coef you know , local sort of local cepstral coefficients that it 's not that great to do it if it 's noisy.


Wiener filtering. It 's a  it 's a cascade of two s

Wiener filter. Yeah , in general , you don't  that 's right you don't wanna othorg orthogonalize if the things are noisy. Actually. Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff , that if you 're converting things to from uh , bands , groups of bands into cepstral coef you know , local sort of local cepstral coefficients that it 's not that great to do it if it 's noisy.


Wiener filter. Yeah , in general , you don't  that 's right you don't wanna othorg orthogonalize if the things are noisy. Actually. Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff , that if you 're converting things to from uh , bands , groups of bands into cepstral coef you know , local sort of local cepstral coefficients that it 's not that great to do it if it 's noisy.

So that  that 's one reason maybe we could combine s some  something to improve SNR a little bit , first stage ,


So that  that 's one reason maybe we could combine s some  something to improve SNR a little bit , first stage ,


So that  that 's one reason maybe we could combine s some  something to improve SNR a little bit , first stage ,

and then do a something in the second stage which could take it further.


So that  that 's one reason maybe we could combine s some  something to improve SNR a little bit , first stage ,

and then do a something in the second stage which could take it further.

What was your point about  about colored noise there ?


and then do a something in the second stage which could take it further.

What was your point about  about colored noise there ?


What was your point about  about colored noise there ?

the colored noise  the  the v the signal subspace approach has  I mean , it  it actually depends on inverting the matrices. So it  it  ac the covariance matrix of the noise. So if  if it is not positive definite ,


the colored noise  the  the v the signal subspace approach has  I mean , it  it actually depends on inverting the matrices. So it  it  ac the covariance matrix of the noise. So if  if it is not positive definite ,


the colored noise  the  the v the signal subspace approach has  I mean , it  it actually depends on inverting the matrices. So it  it  ac the covariance matrix of the noise. So if  if it is not positive definite ,

I mean it has a  it 's  It doesn't behave very well if it is not positive definite ak It works very well with white noise because we know for sure that it has a positive definite.


the colored noise  the  the v the signal subspace approach has  I mean , it  it actually depends on inverting the matrices. So it  it  ac the covariance matrix of the noise. So if  if it is not positive definite ,

I mean it has a  it 's  It doesn't behave very well if it is not positive definite ak It works very well with white noise because we know for sure that it has a positive definite.

So you should do spectral subtraction and then add noise.


I mean it has a  it 's  It doesn't behave very well if it is not positive definite ak It works very well with white noise because we know for sure that it has a positive definite.

So you should do spectral subtraction and then add noise.

So the way they get around is like they do an inverse filtering , first of the colo colored noise


So you should do spectral subtraction and then add noise.

So the way they get around is like they do an inverse filtering , first of the colo colored noise


So the way they get around is like they do an inverse filtering , first of the colo colored noise


and then finally when you reconstruct the speech back , you do this filtering again.


and then finally when you reconstruct the speech back , you do this filtering again.

I was only half kidding. I mean if you  sort of  you do the s spectral subtraction , that also gets rid


I was only half kidding. I mean if you  sort of  you do the s spectral subtraction , that also gets rid


and then you  then  then add a little bit l noise  noise addition  I mean , that sort of what J  JRASTA does , in a way.


and then you  then  then add a little bit l noise  noise addition  I mean , that sort of what J  JRASTA does , in a way.

If you look at what JRASTA doing essentially i i it 's equivalent to sort of adding a little  adding a little noise ,


If you look at what JRASTA doing essentially i i it 's equivalent to sort of adding a little  adding a little noise ,


If you look at what JRASTA doing essentially i i it 's equivalent to sort of adding a little  adding a little noise ,

in order to get rid of the effects of noise.


in order to get rid of the effects of noise.


in order to get rid of the effects of noise.

Yeah. Uh , yeah. So there is this. And maybe we  well we find some people so that  uh , agree to maybe work with us , and they have implementation of VTS techniques so it 's um , Vector Taylor Series that are used to mmm ,  uh f to model the transformation between clean cepstra and noisy cepstra. So. Well , if you take the standard model of channel plus noise , uh , it 's  it 's a nonlinear eh uh , transformation in the cepstral domain.


Yeah. Uh , yeah. So there is this. And maybe we  well we find some people so that  uh , agree to maybe work with us , and they have implementation of VTS techniques so it 's um , Vector Taylor Series that are used to mmm ,  uh f to model the transformation between clean cepstra and noisy cepstra. So. Well , if you take the standard model of channel plus noise , uh , it 's  it 's a nonlinear eh uh , transformation in the cepstral domain.


Yeah. Uh , yeah. So there is this. And maybe we  well we find some people so that  uh , agree to maybe work with us , and they have implementation of VTS techniques so it 's um , Vector Taylor Series that are used to mmm ,  uh f to model the transformation between clean cepstra and noisy cepstra. So. Well , if you take the standard model of channel plus noise , uh , it 's  it 's a nonlinear eh uh , transformation in the cepstral domain.

And uh , there is a way to approximate this using uh , first - order or second - order Taylor Series and it can be used for  uh , getting rid of the noise and the channel effect.


And uh , there is a way to approximate this using uh , first - order or second - order Taylor Series and it can be used for  uh , getting rid of the noise and the channel effect.

Uh w working in the cepstral domain ? So there is one guy in Grenada ,


Uh w working in the cepstral domain ? So there is one guy in Grenada ,

and another in  uh , Lucent that I met at ICASSP.


and another in  uh , Lucent that I met at ICASSP.


Is it  is it the CMU ? Yeah , yeah , OK.


Is it  is it the CMU ? Yeah , yeah , OK.

Yeah , yeah , yeah. Originally the idea was from CMU.


Yeah , yeah , yeah. Originally the idea was from CMU.


Well , it 's again a different thing   that could be tried. Um ,


Well , it 's again a different thing   that could be tried. Um ,

Yeah , so at any rate , you 're looking general , uh , standing back from it , looking at ways to combine one form or another of uh , noise removal , uh , with  with these other things we have ,


Yeah , so at any rate , you 're looking general , uh , standing back from it , looking at ways to combine one form or another of uh , noise removal , uh , with  with these other things we have ,


Yeah , so at any rate , you 're looking general , uh , standing back from it , looking at ways to combine one form or another of uh , noise removal , uh , with  with these other things we have ,

uh , looks like a worthy thing to  to do here.


Yeah , so at any rate , you 're looking general , uh , standing back from it , looking at ways to combine one form or another of uh , noise removal , uh , with  with these other things we have ,

uh , looks like a worthy thing to  to do here.

Uh , yeah. But , yeah. But for sure there 's required to  that requires to re - check everything else , and re - optimize the other things


uh , looks like a worthy thing to  to do here.

Uh , yeah. But , yeah. But for sure there 's required to  that requires to re - check everything else , and re - optimize the other things


uh , looks like a worthy thing to  to do here.

Uh , yeah. But , yeah. But for sure there 's required to  that requires to re - check everything else , and re - optimize the other things

and , for sure the on - line normalization may be the LDA filter. Um ,


Uh , yeah. But , yeah. But for sure there 's required to  that requires to re - check everything else , and re - optimize the other things

and , for sure the on - line normalization may be the LDA filter. Um ,

Well one of the  seems like one of the things to go through next week when Hari 's here ,


and , for sure the on - line normalization may be the LDA filter. Um ,

Well one of the  seems like one of the things to go through next week when Hari 's here ,

cuz Hari 'll have his own ideas too  or  I guess not next week ,


Well one of the  seems like one of the things to go through next week when Hari 's here ,

cuz Hari 'll have his own ideas too  or  I guess not next week ,

week and a half , uh , will be sort of go through these alternatives , what we 've seen so far , and come up with some game plans. Um. You know. So , I mean one way would  he Here are some alternate visions. I mean one would be , you look at a few things very quickly , you pick on something that looks like it 's promising and then everybody works really hard on the same  different aspects of the same thing. Another thing would be to have t to  to pick two pol two plausible things , and  and you know , have t sort of two working things for a while until we figure out what 's better ,


cuz Hari 'll have his own ideas too  or  I guess not next week ,

week and a half , uh , will be sort of go through these alternatives , what we 've seen so far , and come up with some game plans. Um. You know. So , I mean one way would  he Here are some alternate visions. I mean one would be , you look at a few things very quickly , you pick on something that looks like it 's promising and then everybody works really hard on the same  different aspects of the same thing. Another thing would be to have t to  to pick two pol two plausible things , and  and you know , have t sort of two working things for a while until we figure out what 's better ,


week and a half , uh , will be sort of go through these alternatives , what we 've seen so far , and come up with some game plans. Um. You know. So , I mean one way would  he Here are some alternate visions. I mean one would be , you look at a few things very quickly , you pick on something that looks like it 's promising and then everybody works really hard on the same  different aspects of the same thing. Another thing would be to have t to  to pick two pol two plausible things , and  and you know , have t sort of two working things for a while until we figure out what 's better ,

and then , you know , uh , but , w um , uh , he 'll have some ideas on that too.


week and a half , uh , will be sort of go through these alternatives , what we 've seen so far , and come up with some game plans. Um. You know. So , I mean one way would  he Here are some alternate visions. I mean one would be , you look at a few things very quickly , you pick on something that looks like it 's promising and then everybody works really hard on the same  different aspects of the same thing. Another thing would be to have t to  to pick two pol two plausible things , and  and you know , have t sort of two working things for a while until we figure out what 's better ,

and then , you know , uh , but , w um , uh , he 'll have some ideas on that too.

The other thing is to , uh  Most of the speech enhancement techniques have reported results on small vocabulary tasks. But we  we going to address this Wall Street Journal in our next stage , which is also going to be a noisy task so s very few people have reported something on using some continuous speech at all. So , there are some  I mean , I was looking at some literature on speech enhancement applied to large vocabulary tasks and spectral subtraction doesn't seems to be the thing to do for large vocabulary tasks. And it 's  Always people have shown improvement with Wiener filtering and maybe subspace approach over spectral subtraction everywhere. But if we  if we have to use simple spectral subtraction , we may have to do some optimization  to make it work @ @.


and then , you know , uh , but , w um , uh , he 'll have some ideas on that too.

The other thing is to , uh  Most of the speech enhancement techniques have reported results on small vocabulary tasks. But we  we going to address this Wall Street Journal in our next stage , which is also going to be a noisy task so s very few people have reported something on using some continuous speech at all. So , there are some  I mean , I was looking at some literature on speech enhancement applied to large vocabulary tasks and spectral subtraction doesn't seems to be the thing to do for large vocabulary tasks. And it 's  Always people have shown improvement with Wiener filtering and maybe subspace approach over spectral subtraction everywhere. But if we  if we have to use simple spectral subtraction , we may have to do some optimization  to make it work @ @.

So they 're making  there  Somebody 's generating Wall Street Journal with additive  artificially added noise or something ?


The other thing is to , uh  Most of the speech enhancement techniques have reported results on small vocabulary tasks. But we  we going to address this Wall Street Journal in our next stage , which is also going to be a noisy task so s very few people have reported something on using some continuous speech at all. So , there are some  I mean , I was looking at some literature on speech enhancement applied to large vocabulary tasks and spectral subtraction doesn't seems to be the thing to do for large vocabulary tasks. And it 's  Always people have shown improvement with Wiener filtering and maybe subspace approach over spectral subtraction everywhere. But if we  if we have to use simple spectral subtraction , we may have to do some optimization  to make it work @ @.

So they 're making  there  Somebody 's generating Wall Street Journal with additive  artificially added noise or something ?

Sort of a  sort of like what they did with TI - digits , and ?


So they 're making  there  Somebody 's generating Wall Street Journal with additive  artificially added noise or something ?

Sort of a  sort of like what they did with TI - digits , and ?


Sort of a  sort of like what they did with TI - digits , and ?


Sort of a  sort of like what they did with TI - digits , and ?

I m I guess Guenter Hirsch is in charge of that. Guenter Hirsch and TI.


I m I guess Guenter Hirsch is in charge of that. Guenter Hirsch and TI.


I m I guess Guenter Hirsch is in charge of that. Guenter Hirsch and TI.

Maybe Roger  r Roger , maybe in charge of.


I m I guess Guenter Hirsch is in charge of that. Guenter Hirsch and TI.

Maybe Roger  r Roger , maybe in charge of.

And then they 're  they 're uh , uh , generating HTK scripts to


Maybe Roger  r Roger , maybe in charge of.

And then they 're  they 're uh , uh , generating HTK scripts to

Yeah. Yeah , I don't know. There are  they have  there is no  I don't know if they are converging on HTK or are using some Mississippi State ,


And then they 're  they 're uh , uh , generating HTK scripts to

Yeah. Yeah , I don't know. There are  they have  there is no  I don't know if they are converging on HTK or are using some Mississippi State ,


Yeah. Yeah , I don't know. There are  they have  there is no  I don't know if they are converging on HTK or are using some Mississippi State ,

yeah. Yeah , so that 'll be a little  little task in itself.


yeah. Yeah , so that 'll be a little  little task in itself.


yeah. Yeah , so that 'll be a little  little task in itself.

Um , well we 've  Yeah , it 's true for the additive noise , y artificially added noise we 've always used small vocabulary too. But for n there 's been noisy speech this larv large vocabulary that we 've worked with in Broadcast News. So we we did the Broadcast News evaluation


Um , well we 've  Yeah , it 's true for the additive noise , y artificially added noise we 've always used small vocabulary too. But for n there 's been noisy speech this larv large vocabulary that we 've worked with in Broadcast News. So we we did the Broadcast News evaluation

and some of the focus conditions were noisy and  and


and some of the focus conditions were noisy and  and

But we  but we didn't do spectral subtraction. We were doing our funny stuff , right ? We were doing multi multi uh , multi - stream and  and so forth.


But we  but we didn't do spectral subtraction. We were doing our funny stuff , right ? We were doing multi multi uh , multi - stream and  and so forth.

But it , you know , we di stuff we did helped. I mean it , did something.


But it , you know , we di stuff we did helped. I mean it , did something.

So. Um , now we have this um , meeting data. You know , like the stuff we 're  recording right now ,


So. Um , now we have this um , meeting data. You know , like the stuff we 're  recording right now ,

and  and uh , that we have uh , for the  uh , the quote - unquote noisy data there is just  noisy and reverberant actually. It 's the far field mike. And uh , we have uh , the digits that we do at the end of these things. And that 's what most o again , most of our work has been done with that , with  with uh , connected digits.


and  and uh , that we have uh , for the  uh , the quote - unquote noisy data there is just  noisy and reverberant actually. It 's the far field mike. And uh , we have uh , the digits that we do at the end of these things. And that 's what most o again , most of our work has been done with that , with  with uh , connected digits.

Um , but uh , we have recognition now with some of the continuous speech , large vocabulary continuous speech , using Switchboard  uh , Switchboard recognizer ,


Um , but uh , we have recognition now with some of the continuous speech , large vocabulary continuous speech , using Switchboard  uh , Switchboard recognizer ,

uh , no training ,  from this , just  just plain using the Switchboard.


Um , but uh , we have recognition now with some of the continuous speech , large vocabulary continuous speech , using Switchboard  uh , Switchboard recognizer ,

uh , no training ,  from this , just  just plain using the Switchboard.

Oh. You just take the Switchboard trained  ? Yeah ,


uh , no training ,  from this , just  just plain using the Switchboard.

Oh. You just take the Switchboard trained  ? Yeah ,


Oh. You just take the Switchboard trained  ? Yeah ,

yeah. Now there are some adaptation though ,


yeah. Now there are some adaptation though ,


yeah. Now there are some adaptation though ,

that  that uh , Andreas has been playing with ,


that  that uh , Andreas has been playing with ,

but we 're hop uh , actually uh , Dave and I were just talking earlier today about maybe at some point not that distant future , trying some of the techniques that we 've talked about on , uh , some of the large vocabulary data. Um , I mean , I guess no one had done  yet done test one on the distant mike using uh , the SRI recognizer and , uh ,


but we 're hop uh , actually uh , Dave and I were just talking earlier today about maybe at some point not that distant future , trying some of the techniques that we 've talked about on , uh , some of the large vocabulary data. Um , I mean , I guess no one had done  yet done test one on the distant mike using uh , the SRI recognizer and , uh ,


You 'll see a little smoke coming up from the  the CPU or something  trying to  trying to do it ,


You 'll see a little smoke coming up from the  the CPU or something  trying to  trying to do it ,

but uh , yeah. But , you 're right that  that  that 's a real good point , that uh , we  we don't know yeah , uh , I mean , what if any of these ta I guess that 's why they 're pushing that in the uh  in the evaluation.


but uh , yeah. But , you 're right that  that  that 's a real good point , that uh , we  we don't know yeah , uh , I mean , what if any of these ta I guess that 's why they 're pushing that in the uh  in the evaluation.

Uh , But um , Good. OK. Anything else going on ? at you guys ' end ,


but uh , yeah. But , you 're right that  that  that 's a real good point , that uh , we  we don't know yeah , uh , I mean , what if any of these ta I guess that 's why they 're pushing that in the uh  in the evaluation.

Uh , But um , Good. OK. Anything else going on ? at you guys ' end ,

I don't have good result , with the  inc including the new parameters ,


Uh , But um , Good. OK. Anything else going on ? at you guys ' end ,

I don't have good result , with the  inc including the new parameters ,


Uh , But um , Good. OK. Anything else going on ? at you guys ' end ,

I don't have good result , with the  inc including the new parameters ,

I don't have good result. Are  similar or a little bit worse.


I don't have good result , with the  inc including the new parameters ,

I don't have good result. Are  similar or a little bit worse.

With what  what other new p new parameter ?


I don't have good result. Are  similar or a little bit worse.

With what  what other new p new parameter ?


With what  what other new p new parameter ?


So maybe  You probably need to back up a bit


I tried to include another new parameter to the traditional parameter ,


that , like , the auto - correlation , the R - zero and R - one over R - zero


that , like , the auto - correlation , the R - zero and R - one over R - zero

and another estimation of the var the variance of the difference for  of the spec si uh , spectrum of the signal and  and the spectrum of time after filt mel filter bank.


and another estimation of the var the variance of the difference for  of the spec si uh , spectrum of the signal and  and the spectrum of time after filt mel filter bank.

Nuh. Well. Anyway. The  First you have the sp the spectrum of the signal ,


Nuh. Well. Anyway. The  First you have the sp the spectrum of the signal ,

and you have the  on the other side you have the output of the mel filter bank.


and you have the  on the other side you have the output of the mel filter bank.

You can extend the coefficient of the mel filter bank and obtain an approximation of the spectrum of the signal.


You can extend the coefficient of the mel filter bank and obtain an approximation of the spectrum of the signal.


I found a difference at the variance of this different


I found a difference at the variance of this different

because , suppose we  we think that if the variance is high , maybe you have n uh , noise.


because , suppose we  we think that if the variance is high , maybe you have n uh , noise.

And if the variance is small , maybe you have uh , speech.


And if the variance is small , maybe you have uh , speech.

To  to To  The idea is to found another feature for discriminate between voice sound and unvoice sound.


To  to To  The idea is to found another feature for discriminate between voice sound and unvoice sound.

And we try to use this new feature  feature. And I did experiment  I need to change  to obtain this new feature I need to change the size  the window size  size. of the a of the  analysis window size , to have more information.


And we try to use this new feature  feature. And I did experiment  I need to change  to obtain this new feature I need to change the size  the window size  size. of the a of the  analysis window size , to have more information.

Uh , sixty - two point five milliseconds I think.


Uh , sixty - two point five milliseconds I think.

And I do  I did two type of experiment to include this feature directly with the  with the other feature and to train a neural network to select it voice - unvoice - silence  silence


And I do  I did two type of experiment to include this feature directly with the  with the other feature and to train a neural network to select it voice - unvoice - silence  silence

and to  to concat this new feature. But the result are n with the neural network I have more or less the same result.


and to  to concat this new feature. But the result are n with the neural network I have more or less the same result.


Yeah. It 's neve e e sometime it 's worse , sometime it 's a little bit better , but not significantly.


Yeah. It 's neve e e sometime it 's worse , sometime it 's a little bit better , but not significantly.

Uh , is it with TI - digits , or with  ?


Yeah. It 's neve e e sometime it 's worse , sometime it 's a little bit better , but not significantly.

Uh , is it with TI - digits , or with  ?

And  No , I work with eh , Italian and Spanish basically.


Uh , is it with TI - digits , or with  ?

And  No , I work with eh , Italian and Spanish basically.

And if I don't y use the neural network , and use directly the feature the results are worse.


And  No , I work with eh , Italian and Spanish basically.

And if I don't y use the neural network , and use directly the feature the results are worse.


And if I don't y use the neural network , and use directly the feature the results are worse.


I mean we 've had these discussions before , and  and one of the things that struck me was that  uh , about this line of thought that was particularly interesting to me was that we um  whenever you condense things , uh , in an irreversible way , um , you throw away some information. And , that 's mostly viewed on as a good thing , in the way we use it , because we wanna suppress things that will cause variability for uh particular , uh , phonetic units. Um , but , you 'll do throw something away. And so the question is , uh , can we figure out if there 's something we 've thrown away that we shouldn't have. And um. So , when they were looking at the difference between the filter bank and the FFT that was going into the filter bank , I was thinking " oh , OK , so they 're picking on something they 're looking on it to figure out noise , or voice  voiced property whatever. " So that  that 's interesting. Maybe that helps to drive the  the thought process of coming up with the features. But for me sort of the interesting thing was , " well , but is there just something in that difference which is useful ? " So another way of doing it , maybe , would be just to take the FFT uh , power spectrum , and feed it into a neural network ,


I mean we 've had these discussions before , and  and one of the things that struck me was that  uh , about this line of thought that was particularly interesting to me was that we um  whenever you condense things , uh , in an irreversible way , um , you throw away some information. And , that 's mostly viewed on as a good thing , in the way we use it , because we wanna suppress things that will cause variability for uh particular , uh , phonetic units. Um , but , you 'll do throw something away. And so the question is , uh , can we figure out if there 's something we 've thrown away that we shouldn't have. And um. So , when they were looking at the difference between the filter bank and the FFT that was going into the filter bank , I was thinking " oh , OK , so they 're picking on something they 're looking on it to figure out noise , or voice  voiced property whatever. " So that  that 's interesting. Maybe that helps to drive the  the thought process of coming up with the features. But for me sort of the interesting thing was , " well , but is there just something in that difference which is useful ? " So another way of doing it , maybe , would be just to take the FFT uh , power spectrum , and feed it into a neural network ,

and then use it , you know , in combination , or alone , or  or whatever


and then use it , you know , in combination , or alone , or  or whatever


No the  just the same  same way we 're using  I mean , the same way that we 're using the filter bank.


No the  just the same  same way we 're using  I mean , the same way that we 're using the filter bank.

Exact way  the same way we 're using the filter bank.


Exact way  the same way we 're using the filter bank.


Exact way  the same way we 're using the filter bank.

I mean , the filter bank is good for all the reasons that we say it 's good. But it 's different. And , you know , maybe if it 's used in combination , it will get at something that we 're missing. And maybe , you know , using , orth you know , KLT , or uh , um , adding probabilities , I mean , all th all the different ways that we 've been playing with , that we would let the  essentially let the neural network determine what is it that 's useful , that we 're missing here.


I mean , the filter bank is good for all the reasons that we say it 's good. But it 's different. And , you know , maybe if it 's used in combination , it will get at something that we 're missing. And maybe , you know , using , orth you know , KLT , or uh , um , adding probabilities , I mean , all th all the different ways that we 've been playing with , that we would let the  essentially let the neural network determine what is it that 's useful , that we 're missing here.


I mean , the filter bank is good for all the reasons that we say it 's good. But it 's different. And , you know , maybe if it 's used in combination , it will get at something that we 're missing. And maybe , you know , using , orth you know , KLT , or uh , um , adding probabilities , I mean , all th all the different ways that we 've been playing with , that we would let the  essentially let the neural network determine what is it that 's useful , that we 're missing here.

Yeah , but there is so much variability in the power spectrum.


Yeah , but there is so much variability in the power spectrum.

Well , that 's probably why y i it would be unlikely to work as well by itself , but it might help in combination.


Yeah , but there is so much variability in the power spectrum.

Well , that 's probably why y i it would be unlikely to work as well by itself , but it might help in combination.

But I  I  I have to tell you , I can't remember the conference , but , uh , I think it 's about ten years ago , I remember going to one of the speech conferences and  and uh , I saw within very short distance of one another a couple different posters that showed about the wonders of some auditory inspired front - end or something , and a couple posters away it was somebody who compared one to uh , just putting in the FFT and the FFT did slightly better. So I mean the  i i It 's true there 's lots of variability ,


Well , that 's probably why y i it would be unlikely to work as well by itself , but it might help in combination.

But I  I  I have to tell you , I can't remember the conference , but , uh , I think it 's about ten years ago , I remember going to one of the speech conferences and  and uh , I saw within very short distance of one another a couple different posters that showed about the wonders of some auditory inspired front - end or something , and a couple posters away it was somebody who compared one to uh , just putting in the FFT and the FFT did slightly better. So I mean the  i i It 's true there 's lots of variability ,


But I  I  I have to tell you , I can't remember the conference , but , uh , I think it 's about ten years ago , I remember going to one of the speech conferences and  and uh , I saw within very short distance of one another a couple different posters that showed about the wonders of some auditory inspired front - end or something , and a couple posters away it was somebody who compared one to uh , just putting in the FFT and the FFT did slightly better. So I mean the  i i It 's true there 's lots of variability ,

but again we have these wonderful statistical mechanisms for quantifying that a that variability , and you know , doing something reasonable with it.


but again we have these wonderful statistical mechanisms for quantifying that a that variability , and you know , doing something reasonable with it.

So , um , uh , It - it 's same , you know , argument that 's gone both ways about uh , you know , we have these data driven filters , in LDA , and on the other hand , if it 's data driven it means it 's driven by things that have lots of variability , and that are necessarily  not necessarily gonna be the same in training and test , so , in some ways it 's good to have data driven things , and in some ways it 's bad to have data driven things. So ,


So , um , uh , It - it 's same , you know , argument that 's gone both ways about uh , you know , we have these data driven filters , in LDA , and on the other hand , if it 's data driven it means it 's driven by things that have lots of variability , and that are necessarily  not necessarily gonna be the same in training and test , so , in some ways it 's good to have data driven things , and in some ways it 's bad to have data driven things. So ,

part of what we 're discovering , is ways to combine things that are data driven than are not.


part of what we 're discovering , is ways to combine things that are data driven than are not.

Uh , so anyway , it 's just a thought , that  that if we  if we had that  maybe it 's just a baseline uh , which would show us " well , what are we really getting out of the filters " , or maybe i i probably not by itself , but in combination , uh ,


Uh , so anyway , it 's just a thought , that  that if we  if we had that  maybe it 's just a baseline uh , which would show us " well , what are we really getting out of the filters " , or maybe i i probably not by itself , but in combination , uh ,

you know , maybe there 's something to be gained from it , and let the  But , you know , y you 've only worked with us for a short time , maybe in a year or two you w you will actually come up with the right set of things to extract from this information. But , maybe the neural net and the H M Ms could figure it out quicker than you.


you know , maybe there 's something to be gained from it , and let the  But , you know , y you 've only worked with us for a short time , maybe in a year or two you w you will actually come up with the right set of things to extract from this information. But , maybe the neural net and the H M Ms could figure it out quicker than you.


What  one  one um p one thing is like what  before we started using this VAD in this Aurora , the  th what we did was like , I  I guess most of you know about this , adding this additional speech - silence bit to the cepstrum and training the HMM on that.


What  one  one um p one thing is like what  before we started using this VAD in this Aurora , the  th what we did was like , I  I guess most of you know about this , adding this additional speech - silence bit to the cepstrum and training the HMM on that.

That is just a binary feature and that seems to be  improving a lot on the SpeechDat - Car where there is a lot of noise but not much on the TI - digits. So , a adding an additional feature to distin to discriminate between speech and nonspeech was helping. That 's it.


That is just a binary feature and that seems to be  improving a lot on the SpeechDat - Car where there is a lot of noise but not much on the TI - digits. So , a adding an additional feature to distin to discriminate between speech and nonspeech was helping. That 's it.

Yeah , we actually added an additional binary feature to the cepstrum , just the baseline.


Yeah , we actually added an additional binary feature to the cepstrum , just the baseline.


Yeah , we actually added an additional binary feature to the cepstrum , just the baseline.

Yeah , yeah. Well , in  in the case of TI - digits it didn't actually give us anything , because there wasn't any f anything to discriminate between speech ,


Yeah , yeah. Well , in  in the case of TI - digits it didn't actually give us anything , because there wasn't any f anything to discriminate between speech ,


Yeah , yeah. Well , in  in the case of TI - digits it didn't actually give us anything , because there wasn't any f anything to discriminate between speech ,

and it was very short. But Italian was like very  it was a huge improvement on Italian.


Yeah , yeah. Well , in  in the case of TI - digits it didn't actually give us anything , because there wasn't any f anything to discriminate between speech ,

and it was very short. But Italian was like very  it was a huge improvement on Italian.

Hmm. Well  Mm - hmm. But anyway the question is even more , is within speech , can we get some features ? Are we drop dropping information that can might be useful within speech ,


and it was very short. But Italian was like very  it was a huge improvement on Italian.

Hmm. Well  Mm - hmm. But anyway the question is even more , is within speech , can we get some features ? Are we drop dropping information that can might be useful within speech ,


and it was very short. But Italian was like very  it was a huge improvement on Italian.

Hmm. Well  Mm - hmm. But anyway the question is even more , is within speech , can we get some features ? Are we drop dropping information that can might be useful within speech ,

I mean. To  maybe to distinguish between voice sound and unvoiced sounds ?


Hmm. Well  Mm - hmm. But anyway the question is even more , is within speech , can we get some features ? Are we drop dropping information that can might be useful within speech ,

I mean. To  maybe to distinguish between voice sound and unvoiced sounds ?


I mean. To  maybe to distinguish between voice sound and unvoiced sounds ?

And it 's particularly more relevant now since we 're gonna be given the endpoints.


And it 's particularly more relevant now since we 're gonna be given the endpoints.


There was a paper in ICASSP  this ICASSP  over the uh extracting some higher - order uh , information from the cepstral coefficients and I forgot the name. Some is some harmonics I don't know , I can  I can pull that paper out from ICASSP. It


It wa it was taking the , um  It was about finding the higher - order moments of  Yeah.


It wa it was taking the , um  It was about finding the higher - order moments of  Yeah.

And I 'm not sure about whether it is the higher - order moments , or


And I 'm not sure about whether it is the higher - order moments , or


Yeah. I mean , he was showing up uh some  something on noisy speech ,


So it was on PLP derived cepstral coefficients.


So it was on PLP derived cepstral coefficients.

Yeah , but again  You could argue that th that 's exactly what the neural network does.


So it was on PLP derived cepstral coefficients.

Yeah , but again  You could argue that th that 's exactly what the neural network does.

So n neural network uh , is in some sense equivalent to computing , you know , higher - order moments of what you


Yeah , but again  You could argue that th that 's exactly what the neural network does.

So n neural network uh , is in some sense equivalent to computing , you know , higher - order moments of what you


So n neural network uh , is in some sense equivalent to computing , you know , higher - order moments of what you

yeah. So. I mean , it doesn't do it very specifically ,


yeah. So. I mean , it doesn't do it very specifically ,


Uh , anything on your end you want to talk about ? Uh.


Uh , anything on your end you want to talk about ? Uh.

Um , nothing I wanna really talk about. I can  I can just uh , um , share a little bit  Sunil hasn't  hasn't heard about uh , what I 've been doing.


Uh , anything on your end you want to talk about ? Uh.

Um , nothing I wanna really talk about. I can  I can just uh , um , share a little bit  Sunil hasn't  hasn't heard about uh , what I 've been doing.

Um , so , um , I told you I was  I was  I was getting prepared to take this qualifier exam. So basically that 's just , um , trying to propose um , uh , your next your  your following years of  of your PHD work , trying  trying to find a project to  to define and  and to work on. So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events. So , um , the idea is you have all these  these different events , for example voicing , nasality , R - coloring , you know burst or noise , uh , frication , that kinda stuff , um , building robust um , primary detectors for these acoustic events , and using the outputs of these robust detectors to do speech recognition. Um , and , um , these  these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to  to detect these  these , uh , acoustic events. And , um , so I  I been  I been thinking about that and some of the issues that I 've been running into are , um , exactly what  what kind of acoustic events I need , what  um , what acoustic events will provide a  a good enough coverage to  in order to do the later recognition steps. And , also , um , once I decide a set of acoustic events , um , h how do I  how do I get labels ? Training data for  for these acoustic events. And , then later on down the line , I can start playing with the  the models themselves , the  the primary detectors. Um , so , um , I kinda see  like , after  after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into  into a um , Gaussian mixtures HMM back - end , um , and doing recognition. Um. So , that 's  that 's just generally what I 've been looking at.


Um , nothing I wanna really talk about. I can  I can just uh , um , share a little bit  Sunil hasn't  hasn't heard about uh , what I 've been doing.

Um , so , um , I told you I was  I was  I was getting prepared to take this qualifier exam. So basically that 's just , um , trying to propose um , uh , your next your  your following years of  of your PHD work , trying  trying to find a project to  to define and  and to work on. So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events. So , um , the idea is you have all these  these different events , for example voicing , nasality , R - coloring , you know burst or noise , uh , frication , that kinda stuff , um , building robust um , primary detectors for these acoustic events , and using the outputs of these robust detectors to do speech recognition. Um , and , um , these  these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to  to detect these  these , uh , acoustic events. And , um , so I  I been  I been thinking about that and some of the issues that I 've been running into are , um , exactly what  what kind of acoustic events I need , what  um , what acoustic events will provide a  a good enough coverage to  in order to do the later recognition steps. And , also , um , once I decide a set of acoustic events , um , h how do I  how do I get labels ? Training data for  for these acoustic events. And , then later on down the line , I can start playing with the  the models themselves , the  the primary detectors. Um , so , um , I kinda see  like , after  after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into  into a um , Gaussian mixtures HMM back - end , um , and doing recognition. Um. So , that 's  that 's just generally what I 've been looking at.


Um , so , um , I told you I was  I was  I was getting prepared to take this qualifier exam. So basically that 's just , um , trying to propose um , uh , your next your  your following years of  of your PHD work , trying  trying to find a project to  to define and  and to work on. So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events. So , um , the idea is you have all these  these different events , for example voicing , nasality , R - coloring , you know burst or noise , uh , frication , that kinda stuff , um , building robust um , primary detectors for these acoustic events , and using the outputs of these robust detectors to do speech recognition. Um , and , um , these  these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to  to detect these  these , uh , acoustic events. And , um , so I  I been  I been thinking about that and some of the issues that I 've been running into are , um , exactly what  what kind of acoustic events I need , what  um , what acoustic events will provide a  a good enough coverage to  in order to do the later recognition steps. And , also , um , once I decide a set of acoustic events , um , h how do I  how do I get labels ? Training data for  for these acoustic events. And , then later on down the line , I can start playing with the  the models themselves , the  the primary detectors. Um , so , um , I kinda see  like , after  after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into  into a um , Gaussian mixtures HMM back - end , um , and doing recognition. Um. So , that 's  that 's just generally what I 've been looking at.


Um , so , um , I told you I was  I was  I was getting prepared to take this qualifier exam. So basically that 's just , um , trying to propose um , uh , your next your  your following years of  of your PHD work , trying  trying to find a project to  to define and  and to work on. So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events. So , um , the idea is you have all these  these different events , for example voicing , nasality , R - coloring , you know burst or noise , uh , frication , that kinda stuff , um , building robust um , primary detectors for these acoustic events , and using the outputs of these robust detectors to do speech recognition. Um , and , um , these  these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to  to detect these  these , uh , acoustic events. And , um , so I  I been  I been thinking about that and some of the issues that I 've been running into are , um , exactly what  what kind of acoustic events I need , what  um , what acoustic events will provide a  a good enough coverage to  in order to do the later recognition steps. And , also , um , once I decide a set of acoustic events , um , h how do I  how do I get labels ? Training data for  for these acoustic events. And , then later on down the line , I can start playing with the  the models themselves , the  the primary detectors. Um , so , um , I kinda see  like , after  after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into  into a um , Gaussian mixtures HMM back - end , um , and doing recognition. Um. So , that 's  that 's just generally what I 've been looking at.

By  by the way , uh , the voiced - unvoiced version of that for instance could tie right in to what Carmen was looking at.


By  by the way , uh , the voiced - unvoiced version of that for instance could tie right in to what Carmen was looking at.


you know , um , if you  if a multi - band approach was helpful as  as I think it is , it seems to be helpful for determining voiced - unvoiced ,


Yeah. Yeah. Um , were  were you gonna say something ?


Yeah. Yeah. Um , were  were you gonna say something ?

Oh. It looked  OK , never mind. Um , yeah. And so , this  this past week um , I 've been uh , looking a little bit into uh , TRAPS um , and doing  doing TRAPS on  on these e events too , just , um , seeing  seeing if that 's possible. Uh , and um , other than that , uh , I was kicked out of I - house for living there for four years.


Yeah. Yeah. Um , were  were you gonna say something ?

Oh. It looked  OK , never mind. Um , yeah. And so , this  this past week um , I 've been uh , looking a little bit into uh , TRAPS um , and doing  doing TRAPS on  on these e events too , just , um , seeing  seeing if that 's possible. Uh , and um , other than that , uh , I was kicked out of I - house for living there for four years.

Oh no. So you live in a cardboard box in the street now


Oh. It looked  OK , never mind. Um , yeah. And so , this  this past week um , I 've been uh , looking a little bit into uh , TRAPS um , and doing  doing TRAPS on  on these e events too , just , um , seeing  seeing if that 's possible. Uh , and um , other than that , uh , I was kicked out of I - house for living there for four years.

Oh no. So you live in a cardboard box in the street now


Oh no. So you live in a cardboard box in the street now

Uh , well , s s som something like that.


Uh , well , s s som something like that.


Uh , well , s s som something like that.

In Albany , yeah. Yeah. And uh. Yep. That 's it.


Uh , well , s s som something like that.

In Albany , yeah. Yeah. And uh. Yep. That 's it.

Suni - i d ' you v did uh  did you find a place ?


In Albany , yeah. Yeah. And uh. Yep. That 's it.

Suni - i d ' you v did uh  did you find a place ?


Suni - i d ' you v did uh  did you find a place ?

not yet. Uh , yesterday I called up a lady who ha who will have a vacant room from May thirtieth and she said she 's interviewing two more people. So. And she would get back to me on Monday. So that 's  that 's only thing I have and Diane has a few more houses. She 's going to take some pictures and send me after I go back. So it 's  that 's


not yet. Uh , yesterday I called up a lady who ha who will have a vacant room from May thirtieth and she said she 's interviewing two more people. So. And she would get back to me on Monday. So that 's  that 's only thing I have and Diane has a few more houses. She 's going to take some pictures and send me after I go back. So it 's  that 's