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yes you great how this is a daniel garcia rimmer

thanks a very much to well the comedian for select in our work i don't

with great grand island are the ugliness

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has had a using phones in my career and a testing data

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generated so thank you are very much

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you

thank you very much

i'll start by us saying if you variance about

the ward off

so yesterday i was really happy to see so many people

at and the jack godfrey and that are of evaluation benchmarking session

the tell me canyon and i curvature

with a panel of six

distinguished colleagues and ryan godfrey representing the

jack godfrey family and unashamed a hack announcing the establishment of this

ward

this

has a significant meaning to all of us

checks passions in life

included science and understanding

not only just trying to achieve the old men performance

but understanding how systems work why they work and the scientific basis for making them

perform better

so that's is passion and of course where is that all begin with student

and jack loved working with students interacting with students

i you know i had the pleasure of well working with jack for a decade

working for him for a decade in working with them for two and a half

decades

and every interaction i had with jack that involve students where there was always something

remarkable

usually we all walked away learning something new in different it might sometimes be about

cultural language

and in the students cases often lead system inspiration which we heard from teachers the

panelists yesterday and just now from a daniel garcia romano and the team at the

johns hopkins h l t c or you where john jack godfrey we used to

work and

with great passion

so and fortunately the recording of the session is available and encased anybody miss that

i encourage you to watch it just see the context for this award and now

let me handed over to tell me canyon

okay so are plans for is a group or more or two

to do this system and a little used in every room is present a real

room from

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so actually the performance will remember performance was miserable so it doesn't really underscores emotional

armour from everyone used as a front on a proposal on of on one produced

from there is serious i'm not so the military imprisonment this is very differently

personally on a remote we also a good as cousins one thousand two will be

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okay so that it so that

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i very you read on

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and

and then to everybody and thank you also to jack three

yes thank you

and then this is that and of the old fast in many

can you can move onto the next time was decisions

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thank you

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a loaded or speakers

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to those speakers of the topics

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and these fools the in automatic speaker recognition

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the topics

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okay now once again by and idea

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so overlappers okay and organisers or the one amazing you know given the sequence of

all the whole weight and you got have to do that whilst at the beginning

named and you have proposed a new data to get a chance completely the impression

and make it remotely so really what one and about my heart and everybody here

we did you have been successful

with emotion

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final step earlier estimate role give us some really nice learning and speech and bindings

have given by flavour of that

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and everybody to death so that's what i don't remember that

we also have the menu or something was privacy issues of training

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so far as we have about eight to seven based within decision obviously duration itself

is the main over and plastic a language based on an easy life are able

we have corpus regularization

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voice conversion set is this

you know there are nice this and evaluation benchmark in you know only a nist

is used in video audio and video so work about it you know there are

also one about speech applications you know the overall you know isr annotations

and also have a nice class yesterday memorial of jack three on the one friend

and the more in this

so maybe you can imagine working

so first of all you know for this little sister this is a very quickly

so the precision at all possible future the best are war by danielle and an

n-gram so congratulation

so this paper is about how to learn you know mismatch duration by nor and

its car i don't like at the end of it was there you know

inside and it was actually complementary colours also results are so we go back to

what one and one video

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which

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know that this paper because and are already some uncertainty in your statistically conditions not

like just one year some as in the around how much you're gmm estimation

this for source on that are not a clustering of them or something and variance

you know we also have some supervised training well model the baffle you know and

they show that the future that extracts from this networks or you know about integer

linear programming and of this is more speaker and then used a list you see

what else that's is you know whether a speaker recognition and there's over the of

you know

that's thing different embedding is willing did not improve and robustness of duration you know

that's important so even for the analysis you know can that and you know people

are really trying to so it is the prior to the problem of duration mismatch

and in the bn nn framework

class was the least we now that is able to for the nn so is

still there so far were explored in this

this is one of the feature for this new error vad learning can then be

seen speaker recognition language

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i don't cts mismatch between training and about how you that

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itself does also the topic

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mismatch a nice feature going back to channel

and can see that is still problem is how to and whitening the there is

this is how far

and so even when you use the state-of-the-art speaker embedding you know and a speaker

model and you learning is the problem is you don't want it on a mishmash

so

so be released to look into that improving clustering you know you know and we

have little or paper that you know that are sensitive to parameters in validation by

one or whatever was in dover algorithm which perform effective wadding across various additional business

i think was andreas is d

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be were there are more

by the you know minimum cost someone their own name

so you know there was information alarm area under different wainwright unit somewhere it happens

people say you know it's to me and mine english you know so that's a

special one so i think is permissible then with a single curve routine that there

is you can also how to separate considering the same tree

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a compromise or you know that it is the first one there is a lot

of you know and as a state-of-the-art on nist through to that selecting challenge you

know frequency masking locking also "'cause" i lost motivation for this task also seem to

be helpful

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on automatic speaker recognition using feedback control was conversation and finland water

so there are very the ml based you know that you know i'm here is

giving rise to go and one of the meeting you missed and that's the beauty

imponderable we're gonna was done by and you know used and then i and watch

speakers of than going to move you know

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association there was a feature to do speaker recognition in far field you know sixteen

and to focus lost

you can everyone

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one of the speaker really in the future so we have to learn how to

do it is as we find that are involved in unison everything but if you

don't of the progress there

we made use progress and now this is a new area where we need one

and how humans or a is the thing

voice processes that is you know and within a very of papers about using spectral

what the variance along rather well against an all us i guess for singing voice

conversion a nice interesting to see he's on your own voice test you know

and i will assume how can sing beautifully you know

so please "'cause" this paper is

yes so this is a

evaluation benchmarking its colour

features the nist speaker recognition evaluation and here s the you know it shows that

the nist two thousand and eighteen a lie detector machine was a very good success

with other features you audio and visual a visual and visualise at or images

and we well as to combine two and here is that we show under different

paper presented their you know combine still that how long as those though and also

all have already spoofing you can measure you know make the design is just and

right column

that was really good session and but personally i was really one global people don't

you know it's features along case you know you know what is mainly

then post it is given versus those different model more robust models are explore you

know and you know basic task was to have a lot of the goal in

mismatched condition like language you know like that so this is due to these features

only shift for a year

another you know statistical thing on the matter you know and here is to think

i like you know and careful you know carefully if you know

if you have the notation so we have to be careful how the segmentation because

one can have or physical access to not have access

so to have one a carefully designed for a human a result and carefully designed

to assess to require in order to buy something recognition so that those design are

actually one that's longer than there is the one going

so you nist speaker recognition you know calibration in the in the back end issues

with an additional across various condition where reducing or side-information and then there's gender dependent

condition integration stage where side information is learned jointly what the rest of the model

all z

an operating base where into that you are we expect or a speaker locations us

to do re there was just considerably also interesting

you see maybe too optimistic irrigation work well over multiple application scenarios you know and

over different or operating point it is optimized parameter of a square mile manhattan distance

metric to maximize the parallel versus area under the raw

quicker or interesting rest of a false positive rate does not ignore the biggest or

weighted sort of this paper from all

okay

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think that could disconnect speaker but it's invariant to environment you know something really you

know how diverse a training is coming over complete having lots of this new image

processing

well one

in this a new problem we should maybe more

and if you is just as in the audience doesn't last long as are you

can be efficiently get should work on that

so other you know so this is specification on no and over a lot of

combined model a baseline forensic speaker comparison assessing shot communications speaker detection the while you're

all alone

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all home and no not try to combine you know in the same embedding it

is sometimes useful

you know that this is another speaker recognition

no decision and we have personal see that some interesting that's gonna period well i'm

not wrong from the chime challenge you know are defined as this is you know

about the speaker maybe a four pairs and how many conditions of the speaker and

speaker

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number one or back while they're number one and in the expansion

by a sufficient has to

and still the lid system in a single there on the nn framework using a

question that will also good as you know the far-field task maybe shares

analysis is of the for unit is just a that's also missing them in the

second one is

that's so i would like to minimize to this as a null and j and

have you of all this together and you know

so i didn't ask whistle

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me

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but i comparison rate was able to the ten minutes

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crystal

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and okay thank you very much to all the participants

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thank you and

thanks for the wonderful work i couldn't ask for a better team to work with

and

all these you know last minute changes you will handle them with such extreme politeness

in great i'm so grateful thank you

thank you

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