i variable

the to have we really fair use the i per speaker characterization using key and

then

sure there's

speaker i four nist sre two so

the right

my and gently one

basically nine

first we like that you a large

my boss range

and that we use the used a

five

and we the tree

about the punch

the network based speaker dataset

and three demonstrate very what also

and

because the mainstream mixture

different

i for one thing works structure what

oops

such as convolution one they work

i did you walk

here

the lowest eer

a vectorized that's or

in speaker baiting

area cordoned sartre soccer but it to freeze

a pension

in that picture

so

we can is t

use a better to better talk of these two to speaker recognition

this paper is process speaker characterization

using active they only work

don't

sure that

then we don't work a protection a call at a robust protection

the speaker

and the

well

right dependability

used

is are

the variation that that's the

the next baseline if the park on speaker recognition evaluation

kentucky by the

you first nation on thirty two hours passed

and there are large

since

nineteen ninety six

for real application different sure i'm sorry features

but what

right feature

it makes the speech

the nist sre ten show

i will take years but wasn't makes the

mastery power

right proposed the first neural network based

speaker weighting

i also has brought before

feature errors

final by a couple of its the

no milk based speaker eight

is the

mainstream or coded

speaker recognition

and thus

first speaker

speaker mister a

t you know based structure

you know network structure

for

two part

first

the speech you will be cost

for label

representation

followed by rocks the

these tickle forty

been

there are two

second but

therefore

tends to who

and you're we

is true first

there

the combined than for others

speaker very

in this study

i for the

well

we praise

the

second it so there

you can with their

robust they're

according to

work

structure

in addition

i also used

attention there too

you're right

the statistical put it there

accordingly

what structure press at the receiver tension

speaker but

in this study

but i australian feature extraction are

based k to find a good features

for speaker rate

through acoustic features there are trendy for all go far

the first male frequency catch a quite feature

i cory and three

basically

okay recognition

you know

the service

mel-scale filter be attach with each accordingly

p

to me

could be well it backwards with your check

for kind of data local station

are used

took it seven

you cultural for each of the top

the you're saying and data points that if the

current to wrap

the original audio file

which each but between

no

utterance

no problems

in this thing

is the simulated impulse response

i used to cover all reaching or

right column

okay

right in aspects problems

so it

speech vision

try to one for speech

two

like that's

well just as a

original reach

the last

the that you a patient

original

what if i

gail

which the training data

very approach or four

but you advantage future or right

by using

such for kernel in addition

there are

seven corpus

origin

that are it

thus are train artificial

instead

nist sre

switchboard

bonastre

it aspect

that was therefore it after

do correctly for

q

we should okay first and sit

i for one clean speech

for our molding

one utterances from eighty six summon speaker

but i

it's a huge amount of it

well you material should it also nist sre sound and eight

it is i two so that night in a heartbeat

the most

available training data which

because the state yes

it can be expressed are all speech

you know in speech

only

well do you or but to me but

and i

so

it for me for feature extraction

right we are sure

a couple minutes the i it weights

there

national institute of standards

and technology matched speaker recognition evaluation task

sre it was sort of a start to that night

experimental results showed that the cost structure their decision cost function

well the

going segment

two

and

zero point

see

right

two

which the nist

this idea to start it

and decide to a nightly evaluation it has the respectively

this figure this table

chaudhari

well allows you know that

the best performance

there are fixed

i compare the first and second

segment variable speed but it

they also come

see if a feature

well all we can

fun

filled up in

these each feature

awful

you know this the feature

we also

so i

the first

segment i

speaker big be weighted a second

the speaker but something the second their speaker at

for the first their speaker

i

result

so

i think

both the speaker

first bears a bit

they for dimension of the image

we can use the score fusion

okay vector itself

since

i file

filter bank feature was a feature vector function

and also be noted that the cost fifty and draws attention c

and eighty dollars

we so what role

extent also mention i'll for sure

the next frame

therefore it should

what are trained based on

the pen

each feature

this type of show

we can find

by using white

role

for in

they will refer to ensure

we can pick the performance

finally

well so that all call

and ninety six and

by using expensive but it is that file and feature and then it is the

back and scoring

why final submission

that is

where it is

much

each year suspension

bic we wish the

so q two

one two cards

once your feet it's

do you got but not for right

for

pretty much are you

this table show

by the final file for this site tools on it

it is i thought it right

you deterioration

that we show that a portion

this paper to use that system so

to a

next slide so that night

ct has task

i'm scroll neural network

structure

which operates on india and at a at least

and you know extra tight shot

it showed up and have your

and you may speak at

there and sixty you know the lp and feature analysis

i used

channel that's k

we did

feature

mixer six sre

so which what a watch therefore

that one

be a huge

six

no prior for

because our compensation is that what we

be well in that the of available training there

the proposed mixer shooter it should

this year

score

you or initially suitable for

to zero

contrary nine five

the

next

this idea to start at sre two thousand nine that the original dataset back

thank you

thank you very much