why don't my presentation

and representing a or b i don and i this is often get energy profiles

also for speech detection

and a distinctly score for my all those

i'm not only

i can

well and myself

two additional okay

and a little or no one by matrix and the importance that issue

use of interest

and the key subject matter of this paper is development of cartilage or to alleviate

these phenomena

this can be that energy based features which is really for with different

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


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


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



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

that is

which is

very difficult to a very easy to model a doesn't wire technical knowledge

most loving where plate

in this story being the

on the order of speech or y

and so on but it is very difficult because the speech is from b

within the only



there are in various in order for slightly a science and such things


a possibly case

the only four so it will be introduced on a relational some special sessions or

the sensitivities on

by image analysis of a false with a single remote control which will be next

however the major for star only

you know internationally will challenge timebin organized in please okay that's score a seasonal quickly

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

and what is a difficult question

well as those of you know there those on b

changeable elements which


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


and also

we well configuration of the physical or a system

and in a similar systems and so on addition

this is useful right

only the really comparison of the

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


so since the i know the

the statistically meaningful car was for things and is a the

impersonation is not available

and therefore the risk is unknown in a problem at least

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

right behind mobile where the risk is very high that's for sure

an individual nineteen combine the enforce it is unity okay

in the different my from some additional which


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

i'm really and in the literature so it is also based on just one there

so in this thing but we consider three times a small signal x based on

this reason why someone

is visible unit database

and the latter at s

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

in was very little mostly extending steganalysis

then we need to find healing was and possibly a speech in addition we accent


and this is at application that actually

how we can use different that is system to


one thousand

like it is linguistically conversational wise samples for speaker who is one there is something

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

in that using whatever statistically hundred dollars acoustic signal

is there are more or less then

then as it were removed and doors a sticks in the context of acoustic signal


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


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

well at a in the energy so much

and of course a bic is nothing that you colour synergy

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


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

i'm late

these features based on systems and what i

and that the energy based features that capture if a parent and the resulting in

and the features more variability

didn't seem as mentioned in section which was then you know yes

you know constant

so be in that an active speech production facilities where s is i was really

you know in the union model questioned by

no not limited of each motion is more speech

and this is a little investigation which means as shown that it will lie

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


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


something clustering based on and minus twenty which is coming up

and false was less value omega

we begin basically

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

given that is good anything the

is generally the this profile which is given by exploiting minus one

plus one into it and minus one

in order to the difference between the simple elements one

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


there is a ducking under in using their so that and in his and you

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


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


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

this is for models a star

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


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



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

and six

so far results online in the master

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


little better results than people just

but when we use this the results where he can be used

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



a development set on it is useful not continuous

and then we also statistically

results is only mentioned it does it is s one s ten

and asked an is used in this is that is the highest

so it is very important role nor their

the equal error at least a relatively low

for the and was features are and this is because as you compare

the nazis is you an existing which was well when on the basis for almost

a phone a just a

in this work

well contingency

however one hundred and it would be here was you just you very large

well you think whether you listen for testing

and it is expected because

s ten is

based on tts four wheel which is the little based on a decision based on

that each

an active speech i is organized as we chose unity right

well in the model suggesting there are basically

the one thousand and then what is and using

created in the gmm based system

and the standard english more

you know

and then use the best performance on the

on the features it only has a very also

i was features on better than the existing to generate mfcc and sixty

what is it again mapping

and a few though features in windows uses the

and a serious a or b and disability related in these score distributions o d

development and the

you versions therefore the


c is easy and easy gives you know it is the

system and english versions of one hundred and four not

be these features

and we also found this on the initial results on the eval set

we also that the proposed features to perform better than

miss consistent use based on spectral energy features and it is a little on the

mean an additional classifier

now and stands for the r m is just a

however unknown the that actually you know

the last one do what s

using that was just a matter

was recently small for the baseline systems and ms

indicating that the emotions are data needed

i think

this is the lead to go sure would be just as soon as it will

it is also their the on the development set the most features to a that

is shown as a and b

depending on the line

no not tonight

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

well i

similar results are only versions are there

on the that was just system fourteen better than this and


feasible is not from phone

really doesn't bother to at most

combat or indian

finally or something but

in this thing but only exploited bouncing you try to form that was just an


he of is known about features are evaluated on the standard as well a system

been viewed as well as she was and only better than existing we just

this is i don't do not will for testing there is units isn't this just

and just which is based on okay show that is just understands exploits

it doesn't deal almost a single

and s is the problem is a really going to do so especially in

in addition to nist

the senator differences and only time

as a result be a sixty nine under

well i wouldn't look at compression and distance well

in speaker recognition for what in the score should the

it just one or more or fess

the organisers or

on is to go with marshall argument each a and of course urination nine just


is it is possible to not contain challenge

i don't think industry

i mean and five shows