why don't my presentation
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and a distinctly score for my all those
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use of interest
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these phenomena
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a speaker and session position an attribute assisi
and we present experimental results on strong standard
but he wouldn't it as a namely is used for fifteen
was a big hassle
and in some ways
so introduction a speech signal okay
well with his that was of information and then applications well
but is unable
the linguistic information and banning was too much
the weighting function can be useful for speech recognition
language recognition but linguistic information can be useful for speaker recognition in all simulations this
estimation
there really is the us officials what into
automatic speaker verification which is the art speaker detection
so they are is there are in this paper we focus on speaker verification and
are also
so the decision in is equal be
enrolment in like background noise
and a noise channel mismatch
but it was because there is the data lines
the different be speaker in particular speaker vanity
because it should be noted that you when you three eighty speakers voice basically we
will speaker verification or speaker recognition
however
and dimension invariant also be clean speech communication channel
it is a speaker's voice well which is a
in the challenge
because of a really
in the speaker
if you have
with the whole middle residuals like
inter session variability
speaking style and pronunciation duration
features physical conditions gender
no
exactly
and their ability or a business activities in speaker recognition we call spoofing and things
with the means to create a lot of one of them
will be able to venice system effectively
and everything system
and the second system is to develop an icon colleges to be able to
and even something like that moment system
the and we'll have at all
something using model will be able to test the robustness of a system where is
an additional smoothing is important
could be able to design unfriendly a good you know all speaker system
well i secure by mixture
but the final types of the next well while the remaining speaker verification system
one is based on
we didn't or acoustics that his impersonation on the initial estimate astronomy
no recognition results acoustics
there is identical twins
and they're double the same animals do technologies such as speech synthesis and wise convolution
note or
news from charities based on this s and was in the
and found where there
there are in a very is and just activities as it would be in an
analysis of speech
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which is
very difficult to a very easy to model a doesn't wire technical knowledge
most loving where plate
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on the order of speech or y
and so on but it is very difficult because the speech is from b
within the only
speech
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one
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the sensitivities on
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however the major for star only
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challenge to was
and the database was really was previously alright generalized for a
different in a little the content analytical
organise for speech synthesis
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changeable elements which
exactly
last year lamenting janice was no
and i just the listeners and
and there was based on was used converting a play detection then the real speech
detection
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and in a similar systems and so on addition
this is useful right
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i really wasn't the risk analyses also been or someone looks for
various kinds of been applied in also mention also be a d o meetings and
with
so since the i know the
the statistically meaningful car was for things and is a the
impersonation is not available
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but as in industry in
lattice based on an additional reason is
and you know no is that is a very high
well models when we don't
otherwise that i this data in industry so on t
the latter detection training without a nice
it is what we mistral content based recording what and then us smartphone and in
control of the conditions
so the available gender errors could be you know
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an individual nineteen combine the enforce it is unity okay
in the different my from some additional which
so
forcible the problem here we can consider a l stand alone today the
which can be considered as a
you in without in berkeley systems contain natural speech
and there are four for speech detection
and the something that's things you go to be here
can be done i know that the we can be gone it was given that
we're getting microphone point on it is really and transmission channel
a sufficient wind
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so in this thing but we consider three times a small signal x based on
this reason why someone
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so we finished isn't are from a speech
and convolutional useful analysis system designer and initial speech synthesis systems sort of speech a
natural language text
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then we need to find healing was and possibly a speech in addition we accent
speech
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how we can use different that is system to
communication
one thousand
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maybe once or speaker so this is in the intervals between the most
and basically kind of or
by considering the impostor basically and speaker
and that actually that would be that you from this is the same why second
one is
later in this context or
and things with something s is used
and so is a really useful
that's for eliciting model as convolutional on the actual speech we the
in both as follows actually plus the acoustic my
and i and i so i'm the impostors realistic will be on relational or i
mean the convolutional was response of the microphone
you recording idealise speaker
in the multimedia speaker and acoustic
so here the problem is to be able to understand the if we wanted to
the acoustics
which means you can detect some of the characteristics of equality
was legally or maybe condition
because you noting you wanna do speech coding speech
the parameters and
to build you
really independent acoustic something both channels according to my acoustic and one
we will understand whether the speech community and a genuine are indeed
and does not
so in this paper the one to exploit be okay spatial the initial so anything
that in your you get as you it is really the in energy off basically
i think is similar well energy or something that so for example in the traditional
signal also literature we learned online in that you where
however in the actual speech production the and belongs not sufficient which the energy requires
a statistical because
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is there are more or less then
then as it were removed and doors a sticks in the context of acoustic signal
x
in the physical environment like simple and emotion and along a low dimensional the probability
like single emotion
automatically i dunno systems to describe why
and efficient which i was solution it was gonna silently and the ministry that's implement
motionless agenda
estimation of cornish energy bussgang energy which each year
two
but and frequency which is there a five cent signal energy is not only functional
roles signal that all the time and frequency and not
which is completely ignoring the actual and long
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because in a sense that easy and square
so but c the speed of light in vacuum
and even a smaller cost
given that and then they rely on which
so
the binary that it may not here
is that the energy is not only depend on only one
and that is ignored in the conditions these approaches
so what we do we consider distributional is the channel and by considering these are
just a
and the ones a single emotion we consider speech portion of it should be speech
recognition the
so the solution is this is in business in the final say
which is presented in this really so well before they're a little briefly mention that
these features are just an initial estimate a model
the thing to their is you know which include pitch an electrician sufficient condition
metaphysics fusion and features
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and that the energy based features that capture if a parent and the resulting in
and the features more variability
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you know constant
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you know in the union model questioned by
no not limited of each motion is more speech
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this is a mission the line and didn't you
well with a mean basically a
the revolution unit that is one that
and in one year
and you show that different phase maybe do this
and
is the house italy having to improvements in the only one sound an instructional
so this is basically a sound signal just and yet and you also mean and
you see that was and with the total number between one managed to make a
basically
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and false was less value omega
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so i'm innocent civilians form it is clear luminosities nothing but a square and minus
itself in boston based on this is nothing but this is gonna ministry rather than
the previously well as you get a functional these we will call is based
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is generally the this profile which is given by exploiting minus one
plus one into it and minus one
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okay so
in this there will be used to you is really a limitation for
these things are a lot and silence a minute do you we use the energy
because
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can imagine also not using
this is a signal so you will be superior i'm writing
you know
so well
no she can see that are you really
the view point explicitly of speech
this is that the of speech
and this is the view point
so as to what these are the a new values ten split speech
as we can see that the audio file is maximum you
indicating that
and it is a high snr and using both linear prediction inverse like iteration well
very high energy and but being able to the energy so use high energy as
well
secondly we use
a lot in this way speech just convolutional
you was responsible a automatic systems only do not be an impulse response of there
are some interest
kind of all places for a moment for isn't it was also sponsors
will be you know in both senses so therefore
i system is already a this represent one
and the display the impulse response of one
therefore if we consider with the v by giving a basically
so for you in this explicitly mechanism where only here
then you will remember only the data streams are not institutions these fluctuations are basically
all
most so lately
we wanna speech or something and so otherwise huge estimation of impulses also otherwise
we really
we in both systems
so fourteen recorded in boston signals are relocation only there is an excellent in the
u i
and blue or within the one on the gas
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the impulse response is considered that and also for all right and so do you
whatever sometimes was the gate functions
however control the explanation involved only more using function you can see that all this
stuff to that real
and negating their
these speech distortion cannot consider only because of being
so
v high on what we
actually in this work we
consider this observation on the ball in the meeting to do not constitute this innovation
and costly for our miss and false illusion meetings
we consider this is added to this for signal which is easy
the final season is in the next we think that and we are going back
to that used and the
of speech for example
the these and the yearly was that i here
other than in an actual speech corresponding to you guys also very
and that's
but it wasn't there is needed is one as constant consistently so the overall while
also give you get a constant high on the energy
and here llr fluctuations in the next speech and in the next speech for the
one extracted via
the model such
no fluctuations almost or the rest of the homes of this work
socialisation investigation well sure well why that
basically
this
models will be also a for
a small degradation and we also this one on the spectral features
we also this one that we that addition a
we found that in an actual speech
comparison of lr
the initial with features new was really matter
got additional getting the
but haven't in capturing the performance chosen separately distribution in an action was itching speech
we also have the same thing basically on the bus one sixteen or database for
the natural
and e
but it is in each condition is therefore anyone important easily speech just from the
missing the native speech
we also that
the buttons on go
this one you again and you which is a screen
and decision that was basically features which is a on
yes there's recognition and signal
we passing through the band filters
and are
on the this thing and mel filterbank
we use an explicit about
and then
this filters out a little sub band signal that again as you face
and then this can now investigate nearly one of those that's why we model
then we move a mean and averaging all those in that you and the non
dct comparing the
energy research which uses the assumption contribution in the time
we standard it is generally is this problem can database and because of the database
and we use this is that it is feature dimension low dimensional feature does not
model so that it does not want using gmm and the frequencies
i think is used to use lost in the mfccs and then linear sequences elements
yes mel frequency
and we employ union
so the nine dimensional feature vector is more or less one twenty one twenty four
of this increases the finances is thirty nine and it is commonly used in addition
to capture
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we also there the results for the proposed features as i
and it really of their combat mfcc and you design a reversed is easily
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and the six
these are the leading goals
we can see that the results for the on the development and the results for
the future statistically significantly better than
mfcc
four
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results is only mentioned it does it is s one s ten
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for the and was features are and this is because as you compare
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on the features it only has a very also
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and a serious a or b and disability related in these score distributions o d
development and the
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system and english versions of one hundred and four not
be these features
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miss consistent use based on spectral energy features and it is a little on the
mean an additional classifier
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i think
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is shown as a and b
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performs significantly better and then speakers in a nursing
and the fusion is a lot on a similar to the but with features and
there is this is just
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similar results are only versions are there
on the that was just system fourteen better than this and
and
feasible is not from phone
really doesn't bother to at most
combat or indian
finally or something but
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addiction
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in speaker recognition for what in the score should the
it just one or more or fess
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i don't think industry
i mean and five shows