and only one
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interested recognition is performed with a probabilistic verification
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then i describe what is one percent
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and i don't think will addition model and i'm in every and example
so next time maybe more relation between the thing dysphonia to be found okay
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what i had to use
for the one of my speech
so we don't
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and those kind of understanding
i was to design a better and more reliable numbers of detection
we get it was taken motivation and he went down with
a visual within need to different
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considered
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when we listen to it in a less effective in detecting the one okay just
is eight
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sequences as well collect is equal error
there are very seriously
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similar kind of observations regarding both my differences people associated with it is in is
greater than i can challenge
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and the case
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no less
but whatever this is how this is so
is we know is finally this can utilise in the spectrum for example in high
hiding behind
the mailman or indian or whatever
so the use of investments analysis that would be information across different manner
and i will be localised information
and there is no degraded performance
so we can see
more reliable detection with the features that precise information is available
no discuss the differences they have anything can be so as you know in the
mean and they were available in the early nativized the and then window is for
this gaussian means
they're exactly once
and temporal resolution
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the inference is quite well
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so i think in seen once and you press continues better really know what that
was then
the
high resolution in the lower frequency and the higher than within the temporal resolution with
the
that means
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no
in this i in this line we will it's pretty
considering use the solution using the cost of possible
so
given a within the by imposing is as a feature extraction the we live the
constraint on the human audio file
to illustrate the audio file is basically is you in one you for the spectral
resolution of this paper
so do not need to adapt and in the frequency domain
we live in form with something clusters or endorsement power spectral density
which can be no performance is good of you can be
to it you know what is good
like giving infinitely information across the voice vector
and finally we will explain the cepstral recursion
we apply the discrete cosine non-uniform sampling
this is what is it was to use cepstral coefficient feature
no i don't want it is mainly focus on those of police is the result
is visible nineteen change
and we use the standard problem or
for a policeman he was relatively is applied mimicry really implement
the difference of automation
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in the following experiment
we used
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so this is a gmm based system and b l is a gmm based system
so
for one point is exactly the database and the baseline system description you can therefore
before and references
no there is no knowledge of the baseline results on is feasible doesn't i database
is the most substantial variation in the performance of is baseline system
yes we can see in the human eye as an additional the for so no
they for example is the same thing is sixteen and eighty nine
where
this is a gmm based system
give them better performance and bubble
where there is a gmm based system
where s
for either incorrectly for in estimating the l s is a gmm based system used
to better performance
so while it is in difference in performance
using more differently
because
the difference in this paper or solution
insecurity which might suggest that be i think that it is to use this one
hundred
my representing the specifics right and then people
so that a nine
where the difference it is something you would basically the difference in the performance using
c and the mfcc representation
so we use so that analysis
so in this little someone analysis we propose in will be emailing representation then nutritional
i five tokenizer present in the spectral this domain representation you realise
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what i think it'd implement the information they represent different are scored
so in this time
the thing i don't be many presentation of a specific is something i
you
okay you didn't seem to me
genetics within got frequency mean and the lexus there was because can see it makes
and in the and the leftmost autonomy human that it was in the market is
a localising the low frequency of this is what was in there was a localiser
five was spectrum and
that i in the weighted within the i-vectors are presented for the signal
and in my eyes in the email that imposing that are compared to a single
band-pass filter
so
for some time analysis
the remaining where can you denies gaussian with the ones
i by integrating
existing using the specific content of imposing
so
definitely representation signifying the performance of a different is performed combination in the damsel lately
no
something so i in this line mean within a do you might representation all
of six different specific is performed okay well i roll single within the represent the
representational
and using the secrecy sequences in gmm based system
leaving i think is a c and d processing be a more general representing
he may representation using the ellipses in gmms listed in gmm based system
so you can see that
you for specifically for example is the same thing is sixteen in may nineteen
where
this is in gmm based system
the estimated performance and then
well i yes for identity in country and fourteen nist nineteen
when extending this is not use the better performance and on the gmm based
so probably you when he was addition we can see that for those or three
is the main sixty and may nineteen
i think that legalising d i i think of this better where is the thing
"'cause" it is important to the data
but details are really and you the better performance
where is it is still
it could mean and importantly where i think so localising the
i don't with the presidential elections seems to have that the and you the better
performance
no i guess of is defined in a day where is the performance work because
the i for initial immunity the i-vectors are not explicitly localised spectrum so for example
in the need a sequence is he or no ellipses in front end
no that don't temporal resolution and maybe of wasn't feasible fishing
so in this light we will explain why i think is the same front-end format
for
so i x
so
in this data is shown on the classical be split off and highly speech frame
which represent the new nine
and use it in this city
taking this is obviously that was a good lately
please remember that the other thing in a possible solution is represented by the area
defined by
what we are looking like
so
probably one finger against and now we can see that
if they are compressed using d i i was in part of the spectral then
how this particular
it means that only invading is also this area is contaminated
two additional cepstral coefficient
that is only bring reading the
and then it is okay in these diversity in the women
s a single in the windows
we aim to the
investigating more contribution to the computational distribution which means
they're eating i is to deal with one second only
and you have one single
no
this control which is if it is forcing frame when using the uniform recently all
be uniform resembling ones not seem to be
it is normally using the
sequences of feature extraction
so no hannah
well
we don't know why the within the how
exactly
and unionise
needed something in the frequency domain
so in this in this problem can see that
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it was it is before
no there is no what you contribution we got stuck a traditional cepstral coefficient daisy
higher
usually motivation the cepstral a
a computational cepstral coefficient which means
i don't information in government and giving more if the size of one second
is known to be consistently for women is different for the first low frequency scale
is with me
and for the signal treatment is gonna union
and lastly we show that
us spend a of them you need i spent on the
they
this shows because motion is sixteen cepstral coefficient is uniform
i don't is better which means
when i first was in any way to spend on
then it would be better to use i
localised there are a total successfully
and
that you can use the cost in order to a constant is a solution was
different spectral
no only one thing that is k
when i based on the polite the women
then he was also can be
using the challenge is good
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a difference when the other realising over right
where s
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thinking
the engine
the okay to those are persuaded and you the better performance and when i was
i wasn't anything the spectrum that and it is different then
it will look at those i think
then and you get better performance
then the secrecy using a recently
now
it's just an no mandatory have in the i-vector nonetheless global warning behind
use of sequences the using the dramatically scale and he news good if the performance
based on maybe i'll fix the log spectrum
so in this thing here in this role
all singing voices to represent the
there will be made within twenty minutes ambition using the or something custody
that means because he's using that exactly
where is it wouldn't the closing we didn't show that it was in there
they do not even representation using the gmm based system
where
beginning
that's true within their
the only male presentation using the efficiency with the german task which is a anything
so we you remain
s goes we are using the original signal being systems are statistically or we will
go in it was it would be nice to
no
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if we can say they are specific what they contain in and fourteen
where we can see that you are used a specially localising be
logan
and now you know there
in our previous presenting overdemand use of sequences the user dramatically scale
hindi better performance and table two shows are explained that she
think this is in here
this is because as in business
it's a question you know right
no only in thinking one representation
this can afford it is from the decision a fourteen thirteen and fourteen and be
used to think is residual material based front end
a the idea that multiplying being by giving substantially nobody they were using it
it is really is
so no i
i that imposing a when the i-vectors analysing the woodbury then you also decreases the
original article is good this one day they're having the size of those of us
and those are frequently but the woman
no
the conditional condition
so
if you already
seen a linguistically and presentation you might hear i you might the idea would be
presentation
originally proposed in this
well
for something analysis to identify localiser representing this problem
we define the also find that the different exactly the i think within the different
something and
but it was activated in front end which imprecise information relayed consuming
and
it was also they're using the front end and vocal qualities of the database
so this finding explain why
that is simply a back to estimate the solution is
so what i in this thing
bengio
and if you have any portion a have little as follows