however while missus and shall show from actually known anonymous the whole thing of all

i like to three then my paper name a personalised the singing wise generation new

thing one five and

a saliva local as individuals not basic idea of singing wise generation

ran about the related work and the limitations

i know how that the proposed model and it's quite well without

and experiments

so stimuli stun dimension is actually technique to train or anything new

for you the remote call-in to lost then the ten placing me

and the

after gain in this to include

we will

that is a singing out to also you there

from this thing wise generation based on

all this task is actually a challenging

because the generative is singing

should be as an actual has thus and everything and i and also need to

all of them ten

the thing and bowl and we've done or templates

and need to be similar to the you know wise

identity

and

this one

it was a after is different from and ten placing a

so all one way

class analyses fusion

phone usually transformation and the synthesis

or this task

and there are some related to the task

just one is the speech to singing collection and also perform and lastly

analysis

it should transformation and that since there is

as a solution

but difference here is the input is the speech

content

which

well actually it's a lot of the thing content of course training

for then

for you e

you're the speech my heart will go on

will be bozos this person's

singing

my heart we all on

and this speech was in equation purely rely on a

speech to sing

alignment

and the parallel speech to speech and singing

they got

but this is also low from real well for particular

we will within generation

another

well task is the singing wise convolution which can also generate

singing

well this is

basically

that's to come were sourced in seen as was to talk case in this one

this

and there are two basic approach first one is the long run parallel screen okay

which means that they have solved and have a stinging

and the two

a speech analyses

transformation

and the same face

well we'll can get nothing about

and second one is the real the ground parallel screening eight

but you really the time t

speaker identity what is this one need to be learned to the conversion model

coupon

different have a speaker to learn a no need to be trained

repeatedly

well as on the limitation in here is

for the first approach

then you

i mean alignment for second approach

then you to retrain for different target speakers

and

then weight control singing was generation right

this applies the

and i left and commercial model

vol

the weight for a walk over the whole single noise generation

so

the training will be

two steps

first one is the right list and model training

well

what is actually true can work speech i-vectors

same you

g p g r zero one at

to singing and theses

then mm

seconds that training is to

converting the speech

i-vector

saying you have zero mean he

and m c to singing wise

what is the

and

the ural a way to

condition only part and

so you way well assuming we have will

parallel to be shown a singing

well training set

one can performance to training procedures

i-vector n is still

picture

to clean and that a speaker identity

s zero at

if the prosody interest to well

the from time at a ten placing e

and you you're a is the speaker independent eature

so for this right

at runtime we will have full

a time t speech content a singing this song to cause a

and

the ten placing is all your liaison

professionals be necessary

really hopefully can have low professional seen as

singing

prosody and ten

ten whole

we will well

have the f zero and h e

and the purity

from no

template

there again have little i-vector

from speech

why probabilistically include to the

training now rest model

we will have no convert a the

and ceases

well then

we'll still have i-vector and see near zero she

to include the training that we are and what order

and this will all there will generate a final

a little thingy

silencing me hope to be well

the same

speaker without any speech

well

way so

ten minutes in the

sings that'll

i q it is still

one problem

for the pipeline

what is done mismatch between training and testing

because

for which are and we'll codec training

that includes features into a low vocal there

s actually

an actual and

and c is it is natural and it is extracted from actual singing

but at a restaurant and conversion

here is a commodity and this is from now it model and this converted and

sixty

well beyond be different

from the natural and disease

so this

for

calls

some

distortion

you know the channel right okay that's being killed

in order to overcome the mismatch these two

we propose low

into quality network

then this network is to me

you

evangelising we are

conversion and a low coding together

cool basically i

the training will be

not single

and only one is that

which is to

take

speaker identity

from speech

what she's i-vector

and the poles of the

and the linguistic or present representation under temp placing

to train the way for an

channel right

senior a tote

directly so at runtime

we will again have will

you'll this being each

to extract no

you'll thus

i i-vector

and then we'll has another person's trying to say now

so the time placing mean

and we will again how the

prosody much as f zero at a g

and the t v g from the training

then one for this three feet ratings for the training

we'll

it's not one will be lost

but are generated a singing

and this year

there are we would do not have no

when converting that i'm c and then actual and sitting mismatch problem

so

way about

since the size

then the optimal

okay we'll be included

that's as a result

for the experimental way

we experimented with two database

and the model

based testing also

speakers voiced concerns

and of interest

was extracted from

what worked on a

followed by s g u i

and other allies and model

was performed on modeling truncated

we will welcome past three model the first one is a path i'm way because

is cost

and that this one is the one we proposed

the second one is the

okay different

clusters one

what is that i

we have the l s can conversion model like first one

and down way

i have no word one of the in an all pole but our algorithm where

you was lower than the here

so long you press

is

the first one is a label and will call the second one is what marco

the

down way

you why the weight i one without

i to the evaluation approach case first one is an object in the evaluation

second one in the

subject to continue evaluation so for

objective so you one iteration

we can form the root mean square and roll

this is to measure the

distortion it and that have a singing and of the current work teasingly

i the low

so an election year

well which

actually means

well the lower

the l i cu

but you will need to cater the similarity scores

so why you well wait i was really system

where are and

and c

so laces can

the fact i

what is our crumples integrate a one

we can say our integrate it model outperformed the past i model

and though when they this

actually means

our composed model has radios long mismatch

well

and i think it in turn made a features

converting and c and a natural and see

so that we can get better results

our a modal

propose a novel best not all forming

no along with the word will go there

all which we also found

man a similar situations

even wise conversion

right along all the

can be better than one e r vocal the sometimes

all objectives evaluations

so forth if a new regulation

the way you evaluate

all closing in quality and analyzing

similarity

so way actually away

telephone the listening test

well for all of the comedy essentials

and the whole system way

on

randomly selecting the utterance

and l

a selflessness

but is encased being the

as an intact

way from a unique referenced asked to you anyway so only

and that x a b and asked to leave anyway though

and are added

well first

our proposed model way the

a model way so

baseline model way somewhere the one over there

so of the yellow one in our proposed model

why is the baseline

we can say i work of course not all on the basic tagging time so

all quality this is a unique reference task

and similarity

this is an a b preference test

and a full

well

and on the

comparison

i'm terrible

samples model and the pad thai

we can also

although there with us in the trend our proposed a novel

awful form

the all data and a low internal

in comes all

generic and only

so this

significant improvement a unique at our proposed model has

well

has some

benefit

from the by far the integrating

framework

i also plays an animal here

two and a half an hour components model

okay result okay speech

and the knowledge that anything

is the

we had time

is the

propose one

well another baseline we with that our proposed model we can hear

and that and within

right

and our proposed one

okay here an optimal for you feel like in this website

and i would like to come the low

no this paper so our proposed model

actually does not require hernault thinking they have more training work anymore

i'm wondering system

and then we also do not

need to train

different models for have a training

and although there is no frame alignment needing us critics

and

well so on what role speech and mismatch you between we are training and drawn

from which are which implies better quality in there are people who

and then the experimental results also i already have in this all the proposed modeling

handle both

well quality and other thing that are

and real-time you feel have i mean and that's

an email me

and you