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But but No.

If I

Well , no well , no. I mean , it isn't the operating theater. I mean , they don they they don't they don't really know , I think.

Yeah.

QMSum_120

If I

Well , no well , no. I mean , it isn't the operating theater. I mean , they don they they don't they don't really know , I think.

Yeah.

I mean , I th

QMSum_120

Well , no well , no. I mean , it isn't the operating theater. I mean , they don they they don't they don't really know , I think.

Yeah.

I mean , I th

So if if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you

QMSum_120

Yeah.

I mean , I th

So if if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you

Well , I mean , I I think one thing to do is to just not rely on a single number to maybe have two or three numbers ,

QMSum_120

I mean , I th

So if if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you

Well , I mean , I I think one thing to do is to just not rely on a single number to maybe have two or three numbers ,

Yeah.

QMSum_120

So if if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you

Well , I mean , I I think one thing to do is to just not rely on a single number to maybe have two or three numbers ,

Yeah.

you know ,

QMSum_120

Well , I mean , I I think one thing to do is to just not rely on a single number to maybe have two or three numbers ,

Yeah.

you know ,

Right.

QMSum_120

Yeah.

you know ,

Right.

and and and say here 's how much you , uh you improve the , uh the the relatively clean case and here 's or or well - matched case , and here 's how here 's how much you ,

QMSum_120

you know ,

Right.

and and and say here 's how much you , uh you improve the , uh the the relatively clean case and here 's or or well - matched case , and here 's how here 's how much you ,

Mm - hmm.

QMSum_120

Right.

and and and say here 's how much you , uh you improve the , uh the the relatively clean case and here 's or or well - matched case , and here 's how here 's how much you ,

Mm - hmm.

uh

QMSum_120

and and and say here 's how much you , uh you improve the , uh the the relatively clean case and here 's or or well - matched case , and here 's how here 's how much you ,

Mm - hmm.

uh

So not

QMSum_120

Mm - hmm.

uh

So not

So.

QMSum_120

uh

So not

So.

So not try to combine them.

QMSum_120

So not

So.

So not try to combine them.

Yeah. Uh , actually it 's true.

QMSum_120

So.

So not try to combine them.

Yeah. Uh , actually it 's true.

Yeah.

QMSum_120

So not try to combine them.

Yeah. Uh , actually it 's true.

Yeah.

Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.

QMSum_120

Yeah. Uh , actually it 's true.

Yeah.

Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.

The training and testing.

QMSum_120

Yeah.

Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.

The training and testing.

Mmm.

QMSum_120

Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.

The training and testing.

Mmm.

So , I guess what you would do in practice is you 'd try to get as many , uh , examples of similar sort of stuff as you could , and then ,

QMSum_120

The training and testing.

Mmm.

So , I guess what you would do in practice is you 'd try to get as many , uh , examples of similar sort of stuff as you could , and then ,

Yeah.

QMSum_120

Mmm.

So , I guess what you would do in practice is you 'd try to get as many , uh , examples of similar sort of stuff as you could , and then ,

Yeah.

uh So the argument for that being the the the more important thing , is that you 're gonna try and do that , but you wanna see how badly it deviates from that when when when the , uh it 's a little different.

QMSum_120

So , I guess what you would do in practice is you 'd try to get as many , uh , examples of similar sort of stuff as you could , and then ,

Yeah.

uh So the argument for that being the the the more important thing , is that you 're gonna try and do that , but you wanna see how badly it deviates from that when when when the , uh it 's a little different.

So

QMSum_120

Yeah.

uh So the argument for that being the the the more important thing , is that you 're gonna try and do that , but you wanna see how badly it deviates from that when when when the , uh it 's a little different.

So

Um ,

QMSum_120

uh So the argument for that being the the the more important thing , is that you 're gonna try and do that , but you wanna see how badly it deviates from that when when when the , uh it 's a little different.

So

Um ,

so you should weight those other conditions v very you know , really small.

QMSum_120

So

Um ,

so you should weight those other conditions v very you know , really small.

But No. That 's a that 's a that 's an arg

QMSum_120

Um ,

so you should weight those other conditions v very you know , really small.

But No. That 's a that 's a that 's an arg

I mean , that 's more of an information kind of thing.

QMSum_120

so you should weight those other conditions v very you know , really small.

But No. That 's a that 's a that 's an arg

I mean , that 's more of an information kind of thing.

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

QMSum_120

But No. That 's a that 's a that 's an arg

I mean , that 's more of an information kind of thing.

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

Uh - huh.

QMSum_120

I mean , that 's more of an information kind of thing.

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

Uh - huh.

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?

QMSum_120

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

Uh - huh.

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?

Mm - hmm.

QMSum_120

Uh - huh.

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?

Mm - hmm.

With what passing you ? With uh , I mean ,

QMSum_120

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?

Mm - hmm.

With what passing you ? With uh , I mean ,

Mm - hmm.

QMSum_120

Mm - hmm.

With what passing you ? With uh , I mean ,

Mm - hmm.

I I I think that you could make the opposite argument that the well - matched case is a fantasy.

QMSum_120

With what passing you ? With uh , I mean ,

Mm - hmm.

I I I think that you could make the opposite argument that the well - matched case is a fantasy.

Mm - hmm.

QMSum_120

Mm - hmm.

I I I think that you could make the opposite argument that the well - matched case is a fantasy.

Mm - hmm.

You know , so ,

QMSum_120

I I I think that you could make the opposite argument that the well - matched case is a fantasy.

Mm - hmm.

You know , so ,

Uh - huh.

QMSum_120

Mm - hmm.

You know , so ,

Uh - huh.

I think the thing is is that if you look at the well - matched case versus the po you know , the the medium and the and the fo and then the mismatched case , um , we 're seeing really , really big differences in performance. Right ? And and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside You know , a lot of the the cases it 's is

QMSum_120

You know , so ,

Uh - huh.

I think the thing is is that if you look at the well - matched case versus the po you know , the the medium and the and the fo and then the mismatched case , um , we 're seeing really , really big differences in performance. Right ? And and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside You know , a lot of the the cases it 's is

Well , that 'll teach them to roll their window up.

QMSum_120

Uh - huh.

I think the thing is is that if you look at the well - matched case versus the po you know , the the medium and the and the fo and then the mismatched case , um , we 're seeing really , really big differences in performance. Right ? And and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside You know , a lot of the the cases it 's is

Well , that 'll teach them to roll their window up.

I mean , in these cases , if you go from the the , uh I mean , I don't remember the numbers right off , but if you if you go from the well - matched case to the medium , it 's not an enormous difference in the in the the training - testing situation , and and and it 's a really big performance drop.

QMSum_120

I think the thing is is that if you look at the well - matched case versus the po you know , the the medium and the and the fo and then the mismatched case , um , we 're seeing really , really big differences in performance. Right ? And and y you wouldn't like that to be the case. You wouldn't like that as soon as you step outside You know , a lot of the the cases it 's is

Well , that 'll teach them to roll their window up.

I mean , in these cases , if you go from the the , uh I mean , I don't remember the numbers right off , but if you if you go from the well - matched case to the medium , it 's not an enormous difference in the in the the training - testing situation , and and and it 's a really big performance drop.

Mm - hmm.

QMSum_120

Well , that 'll teach them to roll their window up.

I mean , in these cases , if you go from the the , uh I mean , I don't remember the numbers right off , but if you if you go from the well - matched case to the medium , it 's not an enormous difference in the in the the training - testing situation , and and and it 's a really big performance drop.

Mm - hmm.

You know , so , um Yeah , I mean the reference one , for instance this is back old on , uh on Italian uh , was like six percent error for the well - matched and eighteen for the medium - matched and sixty for the for highly - mismatched. Uh , and , you know , with these other systems we we helped it out quite a bit , but still there 's there 's something like a factor of two or something between well - matched and medium - matched. And so I think that if what you 're if the goal of this is to come up with robust features , it does mean So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that that the goal is to come up with features that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the the testing condition is not the same as the training.

QMSum_120

I mean , in these cases , if you go from the the , uh I mean , I don't remember the numbers right off , but if you if you go from the well - matched case to the medium , it 's not an enormous difference in the in the the training - testing situation , and and and it 's a really big performance drop.

Mm - hmm.

You know , so , um Yeah , I mean the reference one , for instance this is back old on , uh on Italian uh , was like six percent error for the well - matched and eighteen for the medium - matched and sixty for the for highly - mismatched. Uh , and , you know , with these other systems we we helped it out quite a bit , but still there 's there 's something like a factor of two or something between well - matched and medium - matched. And so I think that if what you 're if the goal of this is to come up with robust features , it does mean So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that that the goal is to come up with features that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the the testing condition is not the same as the training.

Hmm.

QMSum_120

Mm - hmm.

You know , so , um Yeah , I mean the reference one , for instance this is back old on , uh on Italian uh , was like six percent error for the well - matched and eighteen for the medium - matched and sixty for the for highly - mismatched. Uh , and , you know , with these other systems we we helped it out quite a bit , but still there 's there 's something like a factor of two or something between well - matched and medium - matched. And so I think that if what you 're if the goal of this is to come up with robust features , it does mean So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that that the goal is to come up with features that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the the testing condition is not the same as the training.

Hmm.

So , you know , I I could argue strongly that something like the medium mismatch , which is you know not compl pathological but I mean , what was the the medium - mismatch condition again ?

QMSum_120

You know , so , um Yeah , I mean the reference one , for instance this is back old on , uh on Italian uh , was like six percent error for the well - matched and eighteen for the medium - matched and sixty for the for highly - mismatched. Uh , and , you know , with these other systems we we helped it out quite a bit , but still there 's there 's something like a factor of two or something between well - matched and medium - matched. And so I think that if what you 're if the goal of this is to come up with robust features , it does mean So you could argue , in fact , that the well - matched is something you shouldn't be looking at at all , that that the goal is to come up with features that will still give you reasonable performance , you know , with again gentle degregra degradation , um , even though the the testing condition is not the same as the training.

Hmm.

So , you know , I I could argue strongly that something like the medium mismatch , which is you know not compl pathological but I mean , what was the the medium - mismatch condition again ?

Um , it 's Yeah. Medium mismatch is everything with the far microphone , but trained on , like , low noisy condition , like low speed and or stopped car and tested on high - speed conditions , I think , like on a highway and

QMSum_120

Hmm.

So , you know , I I could argue strongly that something like the medium mismatch , which is you know not compl pathological but I mean , what was the the medium - mismatch condition again ?

Um , it 's Yeah. Medium mismatch is everything with the far microphone , but trained on , like , low noisy condition , like low speed and or stopped car and tested on high - speed conditions , I think , like on a highway and

Right.

QMSum_120

So , you know , I I could argue strongly that something like the medium mismatch , which is you know not compl pathological but I mean , what was the the medium - mismatch condition again ?

Um , it 's Yeah. Medium mismatch is everything with the far microphone , but trained on , like , low noisy condition , like low speed and or stopped car and tested on high - speed conditions , I think , like on a highway and

Right.

So

QMSum_120

Um , it 's Yeah. Medium mismatch is everything with the far microphone , but trained on , like , low noisy condition , like low speed and or stopped car and tested on high - speed conditions , I think , like on a highway and

Right.

So

So it 's still the same same microphone in both cases ,

QMSum_120

Right.

So

So it 's still the same same microphone in both cases ,

Same microphone but Yeah.

QMSum_120

So

So it 's still the same same microphone in both cases ,

Same microphone but Yeah.

but , uh , it 's there 's a mismatch between the car conditions. And that 's uh , you could argue that 's a pretty realistic situation

QMSum_120

So it 's still the same same microphone in both cases ,

Same microphone but Yeah.

but , uh , it 's there 's a mismatch between the car conditions. And that 's uh , you could argue that 's a pretty realistic situation

Yeah.

QMSum_120

Same microphone but Yeah.

but , uh , it 's there 's a mismatch between the car conditions. And that 's uh , you could argue that 's a pretty realistic situation

Yeah.

Mm - hmm.

QMSum_120

but , uh , it 's there 's a mismatch between the car conditions. And that 's uh , you could argue that 's a pretty realistic situation

Yeah.

Mm - hmm.

and , uh , I 'd almost argue for weighting that highest. But the way they have it now , it 's I guess it 's it 's They they compute the relative improvement first and then average that with a weighting ?

QMSum_120

Yeah.

Mm - hmm.

and , uh , I 'd almost argue for weighting that highest. But the way they have it now , it 's I guess it 's it 's They they compute the relative improvement first and then average that with a weighting ?

Yeah.

QMSum_120

Mm - hmm.

and , uh , I 'd almost argue for weighting that highest. But the way they have it now , it 's I guess it 's it 's They they compute the relative improvement first and then average that with a weighting ?

Yeah.

And so then the that that makes the highly - matched the really big thing.

QMSum_120

and , uh , I 'd almost argue for weighting that highest. But the way they have it now , it 's I guess it 's it 's They they compute the relative improvement first and then average that with a weighting ?

Yeah.

And so then the that that makes the highly - matched the really big thing.

Mm - hmm.

QMSum_120

Yeah.

And so then the that that makes the highly - matched the really big thing.

Mm - hmm.

Um , so , u i since they have these three categories , it seems like the reasonable thing to do is to go across the languages and to come up with an improvement for each of those.

QMSum_120

And so then the that that makes the highly - matched the really big thing.

Mm - hmm.

Um , so , u i since they have these three categories , it seems like the reasonable thing to do is to go across the languages and to come up with an improvement for each of those.

Mm - hmm.

QMSum_120

Mm - hmm.

Um , so , u i since they have these three categories , it seems like the reasonable thing to do is to go across the languages and to come up with an improvement for each of those.

Mm - hmm.

Just say " OK , in the in the highly - matched case this is what happens , in the m the , uh this other m medium if this happens , in the highly - mismatched that happens ".

QMSum_120

Um , so , u i since they have these three categories , it seems like the reasonable thing to do is to go across the languages and to come up with an improvement for each of those.

Mm - hmm.

Just say " OK , in the in the highly - matched case this is what happens , in the m the , uh this other m medium if this happens , in the highly - mismatched that happens ".

Mm - hmm.

QMSum_120

Mm - hmm.

Just say " OK , in the in the highly - matched case this is what happens , in the m the , uh this other m medium if this happens , in the highly - mismatched that happens ".

Mm - hmm.

And , uh , you should see , uh , a gentle degradation through that.

QMSum_120

Just say " OK , in the in the highly - matched case this is what happens , in the m the , uh this other m medium if this happens , in the highly - mismatched that happens ".

Mm - hmm.

And , uh , you should see , uh , a gentle degradation through that.

Mmm.

QMSum_120

Mm - hmm.

And , uh , you should see , uh , a gentle degradation through that.

Mmm.

Um. But I don't know.

QMSum_120

And , uh , you should see , uh , a gentle degradation through that.

Mmm.

Um. But I don't know.

Yeah.

QMSum_120

Mmm.

Um. But I don't know.

Yeah.

I think that that I I I gather that in these meetings it 's it 's really tricky to make anything ac make any policy change because everybody has has , uh , their own opinion

QMSum_120

Um. But I don't know.

Yeah.

I think that that I I I gather that in these meetings it 's it 's really tricky to make anything ac make any policy change because everybody has has , uh , their own opinion

Mm - hmm.

QMSum_120

Yeah.

I think that that I I I gather that in these meetings it 's it 's really tricky to make anything ac make any policy change because everybody has has , uh , their own opinion

Mm - hmm.

and I don't know.

QMSum_120

I think that that I I I gather that in these meetings it 's it 's really tricky to make anything ac make any policy change because everybody has has , uh , their own opinion

Mm - hmm.

and I don't know.

Yeah.

QMSum_120

Mm - hmm.

and I don't know.

Yeah.

Yeah.

QMSum_120

and I don't know.

Yeah.

Yeah.

Uh , so Yeah. Yeah , but there is probably a a big change that will be made is that the the baseline th they want to have a new baseline , perhaps , which is , um , MFCC but with a voice activity detector. And apparently , uh , some people are pushing to still keep this fifty percent number. So they want to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.

QMSum_120

Yeah.

Yeah.

Uh , so Yeah. Yeah , but there is probably a a big change that will be made is that the the baseline th they want to have a new baseline , perhaps , which is , um , MFCC but with a voice activity detector. And apparently , uh , some people are pushing to still keep this fifty percent number. So they want to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.

Mm - hmm. Mm - hmm.

QMSum_120

Yeah.

Uh , so Yeah. Yeah , but there is probably a a big change that will be made is that the the baseline th they want to have a new baseline , perhaps , which is , um , MFCC but with a voice activity detector. And apparently , uh , some people are pushing to still keep this fifty percent number. So they want to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.

Mm - hmm. Mm - hmm.

And if we look at the result that Sunil sent , just putting the VAD in the baseline improved , like , more than twenty percent ,

QMSum_120

Uh , so Yeah. Yeah , but there is probably a a big change that will be made is that the the baseline th they want to have a new baseline , perhaps , which is , um , MFCC but with a voice activity detector. And apparently , uh , some people are pushing to still keep this fifty percent number. So they want to have at least fifty percent improvement on the baseline , but w which would be a much better baseline.

Mm - hmm. Mm - hmm.

And if we look at the result that Sunil sent , just putting the VAD in the baseline improved , like , more than twenty percent ,

Mm - hmm.

QMSum_120

Mm - hmm. Mm - hmm.

And if we look at the result that Sunil sent , just putting the VAD in the baseline improved , like , more than twenty percent ,

Mm - hmm.

which would mean then then mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on on o e e uh

QMSum_120

And if we look at the result that Sunil sent , just putting the VAD in the baseline improved , like , more than twenty percent ,

Mm - hmm.

which would mean then then mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on on o e e uh

So nobody would be there , probably. Right ?

QMSum_120

Mm - hmm.

which would mean then then mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on on o e e uh

So nobody would be there , probably. Right ?

Right now , nobody would be there , but Yeah.

QMSum_120

which would mean then then mean that fifty percent on this new baseline is like , well , more than sixty percent improvement on on o e e uh

So nobody would be there , probably. Right ?

Right now , nobody would be there , but Yeah.

Good. Work to do.

QMSum_120

So nobody would be there , probably. Right ?

Right now , nobody would be there , but Yeah.

Good. Work to do.

Uh - huh.

QMSum_120

Right now , nobody would be there , but Yeah.

Good. Work to do.

Uh - huh.

So whose VAD is Is is this a ?

QMSum_120

Good. Work to do.

Uh - huh.

So whose VAD is Is is this a ?

Uh , they didn't decide yet. I guess i this was one point of the conference call also , but mmm , so I don't know. Um , but Yeah.

QMSum_120

Uh - huh.

So whose VAD is Is is this a ?

Uh , they didn't decide yet. I guess i this was one point of the conference call also , but mmm , so I don't know. Um , but Yeah.

Oh.

QMSum_120

So whose VAD is Is is this a ?

Uh , they didn't decide yet. I guess i this was one point of the conference call also , but mmm , so I don't know. Um , but Yeah.

Oh.

Oh , I I think th that would be good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,

QMSum_120

Uh , they didn't decide yet. I guess i this was one point of the conference call also , but mmm , so I don't know. Um , but Yeah.

Oh.

Oh , I I think th that would be good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,

Yeah.

QMSum_120

Oh.

Oh , I I think th that would be good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,

Yeah.

so

QMSum_120

Oh , I I think th that would be good. I mean , it 's not that the design of the VAD isn't important , but it 's just that it it it does seem to be i uh , a lot of work to do a good job on on that and as well as being a lot of work to do a good job on the feature design ,

Yeah.

so

Yeah.

QMSum_120

Yeah.

so

Yeah.

if we can cut down on that maybe we can make some progress.

QMSum_120

so

Yeah.

if we can cut down on that maybe we can make some progress.

M Yeah.

QMSum_120

Yeah.

if we can cut down on that maybe we can make some progress.

M Yeah.

Hmm.

QMSum_120

if we can cut down on that maybe we can make some progress.

M Yeah.

Hmm.

But I guess perhaps I don't know w Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features. So mmm , you need more features. So you really need to put more more in the in in the front - end.

QMSum_120

M Yeah.

Hmm.

But I guess perhaps I don't know w Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features. So mmm , you need more features. So you really need to put more more in the in in the front - end.

Yeah.

QMSum_120

Hmm.

But I guess perhaps I don't know w Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features. So mmm , you need more features. So you really need to put more more in the in in the front - end.

Yeah.

So i

QMSum_120

But I guess perhaps I don't know w Yeah. Uh , yeah. Per - e s s someone told that perhaps it 's not fair to do that because the , um to make a good VAD you don't have enough to with the the features that are the baseline features. So mmm , you need more features. So you really need to put more more in the in in the front - end.

Yeah.

So i

Um ,

QMSum_120

Yeah.

So i

Um ,

sure. But i bu

QMSum_120

So i

Um ,

sure. But i bu

Wait a minute. I I 'm confused.

QMSum_120

Um ,

sure. But i bu

Wait a minute. I I 'm confused.

Yeah.

QMSum_120

sure. But i bu

Wait a minute. I I 'm confused.

Yeah.

Wha - what do you mean ?

QMSum_120

Wait a minute. I I 'm confused.

Yeah.

Wha - what do you mean ?

Yeah , if i

QMSum_120

Yeah.

Wha - what do you mean ?

Yeah , if i

So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just just for example. So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous

QMSum_120

Wha - what do you mean ?

Yeah , if i

So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just just for example. So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous

Oh , oh. I see.

QMSum_120

Yeah , if i

So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just just for example. So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous

Oh , oh. I see.

Mm - hmm.

QMSum_120

So y so you m s Yeah , but Well , let 's say for ins see , MFCC for instance doesn't have anything in it , uh , related to the pitch. So just just for example. So suppose you 've that what you really wanna do is put a good pitch detector on there and if it gets an unambiguous

Oh , oh. I see.

Mm - hmm.

if it gets an unambiguous result then you 're definitely in a in a in a voice in a , uh , s region with speech. Uh.

QMSum_120