controller presentation of a more speaker on the c paper transform is actual and costly
or emotional most commercial
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on the ball state of technology and to sign
based in the outline of this presentation
first i'd okay and extraction to emotional most commercial
and so relating what
i don't talk about our contributions
proposed framework
experiments
and can push
emotional most commercial is almost conversion technique
it aims to coursing motion in the speech
from these loss functions to the target options
in the meeting about the speaker and then key and linguistic information should be greater
and you can see in this speaker
the same utterance spoken by the same speaker
but the
motion has been changed some signs i is tiny has naples many applications we human
computer interaction
such as personalise
text-to-speech
so for a pulse no conversational agents
and three or no
emotion physical access with multiple signal or should groups which can be or five kate
right it's like trial
mostly
more
you motion is also scroll segmental
i hierarchical you make sure which makes it more difficult
two can where is the emotion in the speech mean those that early studies only
focus on that spectrum commercial
and i haven't okay you mashed attention on the cost the
it's missing is not sufficient
and most previous work we where
tara notion it is
from the source of the target emotion more options
by in the private case heralded have used it is say a difficult to cart
and also will limit the score applications
you know the true really met
we eliminates the need for the panel clean data we propose to your cycle gonna
to find the mappings
of spectral a post e
so i've okay is proposed for
me translation and has
shaves remarkable performance
a non-parallel tossed
researchers
have successfully applied these most commercial and speech synthesis
i was like yoga has three losses
whether where zero loss
cycle consistent signals
and i and he may not
so ministry losses
was that we're gonna turn around nineteen features also target don't mean
results and you want to know data
another challenge you motion commercial without austin modeling
in many for so the information test the result
fundamental frequency which we also quite
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used
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where studies
convert have zero
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but i was we all know
i zero very from the micro most reliable suction the walls
and three states
twenty four options that will
side modeling used to sing for channels characterize
but speech was there are rare
some researchers propose to
well do i zero ways
conclusion remote transform
can was made about transform
used a signal processing technique
which is true
it controls the signal
two different time don't means
it can describe still
well as t you different and resolutions
and we think
so as to me to modeling hierarchical signals
such an afternoon
this figure shows although
continuous wavelet transform walks
we use minimum transform
composed of no
to turn scale
they have the same linguistic content and spoken by the thing speaker and we assume
that more scales
can capture the short-term variations have scales can capture the long-term variations
it has taken this tool options
very infomercial to tune the long-term variations
even though they are spoken by the same speaker and we don't think speaking time
this variations reflect the emotional variance
the different time scales options
so in this paper
we propose a panel of free emotional most commercial framework
and we also showed that of course i
although motion almost commercial
we can come versus an actual and prosody three shows recycle can extracting and we
also
that's great
different
training strategies
for spectral quality commercial
sessions
some pre-training a joint training
another thing
experimental results
shows that we also the baseline approaches and carrot she called quality converged
speech samples
this is the training phase of our proposed framework
you know training phase
majoring to sample against false fine sure prosody separates the
we also want vocoder
so it is trend of spectral features and i zero strong salsa target sergeant's
with only
you called it but aside from fisher seem to twenty four conventional caps
and use mean are transformed into compost zero into ten different scales
and we train this to cycle goes for spectrum was this outrageously to lower than
that of clean speech and start time you start for acoustic features
i really conversion phase
we use
to train sec okay
two congresses five approach crusty
we used was vocal the actual singing size that coverage options
we also in rats case two different training strategies our proposed framework
the first one is
second conjured
in this framework
we concatenate and catch
we say that would keep based f zero features
and that you put to it's like okay
and that's that and the second one cycle again separate
i in this framework
wishing to several gets full spectrum of the active
and in this work we can bound of three from walks
and where you're sidewalk and coworkers fight for
and use a
in your transformation to compare the posterior
this framework we call so baseline
and the
so i will control and second guess separate refers to two different training strategies one
sec okay
and we talked about last slots
and we use the l
most a lot
this whole words which is recorded by a
all stressful in american actually it's
i don't we conduct experiments from neutral two and three signed a sparse
for each emotion combination
but you're slidy non-parallel utterance
around stream means for training and ten utterances for evaluation
and all the all tracking
no iteration
big companies i'm seeking to marilyn spectrum distortion
and the cohort it a nasty and p c of size so
the performance of the was the commercial
from this to table
our
we can say that our propose
so we can a separate stream or
all of the baseline and several controls remote for all wasn't shapes
and we also further contact
sometimes you evaluation to us to study motion similarity and in this experiment we compare
the preference test
and that's from this to speaker
our proposed framework consistently on some the baseline and the second controller
i'm from the figure six we can say that most the listeners
who's our
so we can
separate framework
rather than that cycle control and
so
by results
some pre-training is
why the sampling training is much faster the orange county
we think because of the these menus manage trustees different time scales
and it is different time scales makers this though
current content and containing depends i denotes
so that
join training has not
can estimate the transform coefficients read the spectral
features and stuff frame now
and the
and this train a strategy assumes that holstein is containing and that
so
with a mean each number of training samples
for example streaming use of speech
in our experiments
but during training
so we're gonna model
kernel
generalize very well so emotional mandy
we start unseen components and the
one time
interface
so we thing that's made use of reason why as a separate training is much
better than the joint training our experiments
i realistic
paper we use that's
several training outside for mostly
can actually
after performance than during training
and the experimental results also
shows that our proposed motion almost workshop framework can achieve better performance based on but
is not an okay data
and
and this is all
all for a pronunciation central or attention