i and you for doing this you know i'm second exactly i don't here is
that answers and propose a novel in figure of science and technology and research find
ways that leaking just by
we introduce some recently some works the lower developing your own seems to incorporate the
that lisa
speaker and listened well speaking
so rather than a tutorial on capitol details we share our experiences indefinitely since between
the printer
one problem we face and one solution that we take
is enjoying work we understand processing of exercise does not hold on the high applied
to harness if indeed and submachine comma
and then problem topics to discuss i won't be able to cover everything details within
an hour of tutorial
so if you have some bar that you want more power domain imposed questioning decoding
in section
okay like to discuss what does this interpolator his
this is an example a final meeting people speaking different languages though there are two
main speaker and so interpreted as
it is done for one person speaking is five that once the japanese
mandarin speakers that is pretty intervals individual will translate probably induced feature by means
so the aim is to construct on the since it is interpreted it can be
proficient interpreter
one language translation is one of the knowledge of the and to mimic human integrated
income after from one language to another
this technology properties of the automatic speech recognition and upon this leads into the backs
in the source language
on the same translation the top on the text image source languages the corresponding text
target languages
and thanks to speech synthesis or pdas the generic space where problem based on the
x in the target language
however kind of finding a spoken with this is an extremely complex tasks
with the one this process is translation performance is the five have been the probability
like professional interpreter
so i don't the ls from sleep you the component and use
then we will discuss what we sell out is the worst different beam since the
greater
you know many people was on the baseline with analogies with five days are used
for
the development of automatic speech recognition has an apple as in the process and just
once the basic human speech
i don't walls for is a third approach is happy line are not with people
and technologies and then can be seen with dynamic time warping
and moved to the different approach with statistical modelling offers you don't model one male
portion which more or hmm gmm
this figure shows the generic structure of the original gmm furthermore
in commenting on what's of three main components impulse one is that was the one
with the theme in the acoustic probably don't have been genetically by itself bored or
phoneme based hmm
the second was his pronunciation lexicon with whatever the pronunciation of all with a sequence
of phones
and that one is like was more deal with a steaming the prior probability of
sequence of four
finally speech recognition decodings that we find the base station once all words according to
the speaker conversation a lexicon and language models
and the resurgence of dignity in the nist has also some place in the course
of really the on utilizing unlabeled transform it is are
maybe is that the performance e is a process things that's is very close to
the baseline formant for remote
for example a hybrid hmm and then estimates the posterior probabilities idea and
then there is also c d c or connectionist temporal classification
and also seconds to say what's more they with listen and stuff parental
the important part of thought differently there is simply by many complicated hence and in
more detail
and then why the weighted measure performance is to exactly
in the last twenty k it has been significant improvement in the has a performance
this can be thing and of jingle bayes theorem decrease in word error rate
in nineteen ninety three to work and or you can see what they that was
close to hundred percent
suddenly i am in microsoft this work have shown that speech recognition one is a
more you word error rate and professional transcription which was at all five point five
percent more generally
in a similar detection technology one has gradually sifted
no foundation for a system using we're all working and an analysis in this is
meant for
no weight for musical computational problems and no more flexible for using the thought that
income or the hidden semi-markov models gmm or svm and gmm
and something instead of the performance starts have been successfully constructed based on your programmable
so this time sequence of second from all
can i think that comes solely on import over complete d s
and provisional with men transform
the performance on t v is has also been improved be problem for is to
human likewise
in latest examples
most of restricted or and russian trick or treaters instruments
it's okay here is a little voice
no that isn't videos good has become more human-like as the following speech samples
it also listed on short notice two children and at a very close
in addition the google i would be viewed as we do not at all somebody
close to human speech
this system used a combination of cognitive gives a changing is in the discrete is
and using the hold on with meant to control intonation depending on the second
in a system become his or in trying to scale to have found an appointment
or whole utterance to estimate the separation
i will resemble the second one
menu
i
for every
where i
well p o
so it's of great here we cannot be president anymore with one if you want
in which one with murphy you get similar papers on their website
okay so we have seen that both examine the fa improve the quality close to
your buddy
okay we solve all problems
that is that the balloon model was used to train a
and how we can utilize the time maybe a system
is it is an example as before we just by their own meeting between two
people speaking different languages
that's a more powerful is an iterative process of interpretation
where one of the speech in one languages
they will use a and we don't we can be enough to sentencing was translated
and speak to the other speaker in another language
this means that the estimation process before is simply the end of the sentence
and i don't paper has the ability to do some other in this process we
just listening don't leading and speak
so the channel started from unseen speakers integrator are not uncommon by a size and
the idea is to construct a machine that have the ability to listen one is
speaking as well as their ability to perform recognition and synthesis fits into by
that was discussed ill posed problem one with the following unless you can listen while
speaking
and is an incentive produce the basic mechanics so in this congregation which is called
the speech at
and mechanism have a slight disturbance mesa therefore for the speaker's mind to the listeners
my
it consists of speech production we can see something with the speakers is worth and
you know based soundwave
and a week the speech waveform to me
the data as this perception process happen in the listeners identity system can perceive what
was said
can close to speech in exactly so has a critical out to be but we
can assume for speaker small to the u
that would be able to communicate few hundred to how to use and state
you know how to talk like one company but their articulation and listening to solve
problems
so again here we leave behind with acquisition possible speech and speaker system hasn't could
be allowed to keep k
so the children who was there really of them have difficulty to produce three states
even adults can be count after becoming proficient with the languages nonetheless for speech articulation
be applied as a result of the that all the three
human brain that is inside a model integration in speech processing
so the auditory system is critically important steps and all speakers and the model system
is critically both in the progress and all states
so that was done by three or four on the next we have seen it
sounds like model response talking face okay feeding both doing the busted perception mostly is
and to encode for all samples articulation
so this means that the process false this perception and production is not unique ability
on the other hand competitors also are able to learn how to use and how
to speak
as we know by whales mixture and within the how to use the so given
the state is people point out that
and also by werewolves text as seen this is if you know how does b
but computers cannot hear their own voice
and then release lsp a separately and then
and therefore requires a little more robust and next thing in sort of points way
more attention
but the question is can we do a long lasting that can listen while speaking
now discuss how to develop a sequence in speech and framework
the proposed approach to the logo slow speech in one based on clean
this is particularly model it indicates human speech perception and production here
it is to have a system that not only can be silence p but also
is somewhere speaking
this is a standard is not in tts framework in which the training to independently
as we mentioned before by where is the most you can learn how to lisa
and by where yes in the second the how to speak
now here estimates in speech and free will
so we can look connection from asr to tts and vts database or
this means yes really see the asr output and may so really see if the
output probability s
or in other words a sunken this that the what did you say
the key idea here is to train both baseline vts models jointly
in the training they the frame level training with a label in a globally in
some reasonable baseline mean
where a sliding scale encourages fighter using unlabeled data engineering useful for you rate
including bounced against imposing we use a starting today is modeled independently using standard way
in more detail
if the original speech
one white
is the original text
huh is the predicted streams
well why
if the predictive text
so the a set of is that are from x the whitehead
you know using all sequences are more militant on what is the text
and the there is a into a problem to white to x had so hugh
we also used to you know second one model control for text to squeeze
i don't case it is interesting speech and the only difference cases where the but
also in text they are available
so given a pair of skis and that's the a one model can be trained
independently in support by selecting
is this can be done by minimizing the loss between the predicted that the sequence
and the ground to sequence
so for its this is by minimizing the loss between y and y head
and for the tts this is done by minimizing the x and x and
is there is when one is fifty five central
for this thing will be for unsupervised domain
given on this feature x
it is pretty the most possible transcription y huh
and based on why cooking is trying to reconstruct this feature
and let the loss yes between the original space feature x
and predicted streams extract
so therefore or this is possible to improve tediously speech-only by it is portable phase
are
now see that all cases where only text data is available
so given only text features y
it is generally it's this feature x have
and based on x head as a try to record a text regions y
and we got but also it is out of between original text feature y and
of but that's what y k
so here it is possible to improve a start with excellently by support of tts
so the overall any object is to minimize baseline and it is lost doing was
proposed by let me really write something well and possible by let me when only
unclear probable
so the basic idea is to able to train domain that's without or getting the
old one
if we set off i'll be greatly zero one this means that we can use
a portion of the loss and the canadian provided by the training set
but if we set off i was zero this means that we completely learning curve
with only speeds or latex
but this is the overall structure of a star we use the segments of segments
from which is similar to a lot so that misspell proposed by janet all to
draw some fifty
so it has in order according to an attention model
they one is x which is this is features
and a good is why which is the act sequence
and i is the importance they were it's t is the decoder state and the
attention models produce context information
at time do you which is a line between the encoder and decoder hidden state
and the loss function is colour we be between the white and a pretty good
why where c here is the number of output classes
similar to a silence detector this is that can subsequently be as baseline control for
it is also classes in court are they going to an ancient old one
x r is a linear spectrogram feature what i x and stands to talk about
features and wise that x include
and is the oldest and hence the decoded as they can attention borders context information
based on a hundred and recorded hidden state
note that kind of lost the first one is database your space to function lost
and the second one is the and a sentence detection with cross entropy
okay so let's discuss some experiments we since the chain
in this week's features we use a d-dimensional mel spectrogram
one thousand four dimensional in the spectrogram
yes it second thoughts between problem by using a finland can pretty the phase and
false stft
and for the next we used in the six off of it six complexion more
actually special cost
by our proposed mental we experiment on a corpus with a single speaker
because i'm is only most agencies by the economy are trained on a single speaker
dataset
we less another mostly single speaker data to just mean and use talked also about
what training of all on a more than one and four hundred one for this
or
most similar several situation
for this when for training data with parameters with the that's the or probable
and the second one is when one small portion of it have to wear speakers
and text data and non-target cannot utilize text or space data on
and the last one is when wanting to devise bands present text data
then we showed that is okay so you know use the error rate for a
while waiting a somewhat in
this is the result when the system was trained we will training data
we can see are three point one percent
no one and channel has a transcript and things the l and the remaining one
we have speech or basically only with an example this year become so you one
point seven percent which is quite high
and but it is only one speaking we assume states mechanism
it and you yes can be designed their using a very low and generally useful
feedback
results show that we improve its performance from something one point seven percent and twelve
point three percent
we separately and twenty five percent is only and twenty four percent and only is
okay three point five percent eer
it was very close to the system used for a hundred percent they're data
nineteen and actual pdf they sell
what it is experiment we report the emergency we pretty well male and a lot
welcome to the ground to the ground truth
results show that a starving tedious model have been trained with small gradient ascent
this is are there
using a really engine used would be great
the all along with formal training be has no square zero point six
and with only ten percent brand it on the last become one point in
zero five
then by listening was speaking one we explore only with phase we also included it
is performance
the summary inspired by the human speech in we propose a gaussian speech and often
able to most into conformity somewhere speaking and achieve something strong bias let me
and mechanism a novel and found it is still it is it harder when there
is if i'm really to analyze identity is too important leasing pair and optimized on
whatever we could is construction cost
however the one sentence the sort of the system was able to handle unseen speaker
this is done also only we mix the voice a given speaker via the speaker
identity by one hundred and twenty
furthermore has are also on the other two speaker specific central speaker because to is
unable to produce a more or is this problem in speaker
so there are we tried to improvement and then a lot of this incredible seems
to change
so the eighties to handle voice characteristics are unknown speaker
you know in the area of speaker recognition system into the speech in little
and i spent a globally deal yes to have the same speaker using one speaker
adaptation
after the couple with a star and we develop a speech in primal better and
what do you a speech problem unknown speaker
this morning mankind is somewhere only it is available
and it really most possible package and y
we can recognition profile speaker everybody's they're finally based on y and z two d
s try to construct an x have
indeed yes loss is calculated between original speech feature x and x huh
one the other hand went on the actually is available
you samples speaker factors the
yes each and every speech feature x have based on that x y and speaker
close to
then given x head and is a try to recall a white
it's a loss is calculated between original x y and of predicting one
is a consequence is a is the same as in basic the since speech at
when the second to stick when it is no additional input on the speaker factor
so now they're kind of loss function
one is this recall such a loss we can see
the second one is and of sentence production rules with cross entropy
and you one is the percent one loss which is the cosine distance between the
original and unique speaker and basically
we're on our experiment on the task is multi-speaker data which is the worst original
dataset
we normalize the speech a necessary supplies not mean that for will be there is
i before any assigned to hunter its parent where training set
and i four records is around seven thousand across of all sixteen hours of speech
spoken by native speakers
well as on the consists of all something else and it is about sixty six
our spoken by two hundred speakers
so if you know we use a month or they have ninety three
and then f are likely to for dataset
sure that is already some
we first train baseline model by using examples for is i t for state only
and we choose seventeen point seventy five percent eer
in the second rule we train a little we clearly tell the full was originally
aside two hundred eighty four data and its you seven point what we can see
are
it is our global performance
and in the last four we trained on one deal with some reasonable price than
using as i before s period and as i two hundred and we're at all
for comparison
we are more training with something simplifies the mean
we get a label propagation mental
where we train the original one really there's this text is i before
we realised are pretty good initial one we don't really using data
so for text only right side two hundred we're stationed ideas to generate the corresponding
speakers
and for speech-only is idle under we'll stage channel is not to do that the
corresponding thanks
often there and we train a more the other with a generic full training set
our results shown by using label propagation is absolutely use the cr fourteen point fifty
a gaussian
nevertheless
speech and model could achieve a significant improvement
and it's we nine point eighty six percent c are
which close the door one result
similarly answer yes loss could also be viewed as the training to him as in
switching
no you want to show some speech samples
the first one is the baseline model where we train only with something percent greater
detail
this is a rate of travel and actually provide a solution
then might be lies inside hundred percent are very well with speech change
this is because the server the problem actually provide a solution
and the one model trained with appropriate training set
a basis to read the travel
they actually provide a solution
no one will but it also the speaker d as they still
is this the baseline
the bases aren't the problem they actually provide a solution
and this is basically changed
the basis of the problems i actually provide a solution
and is just a horrible model
the places aren't the problem actually provide a solution
and can see that in one with a very
it is gonna be improved significantly
so that a summary we proved most of speech and still had voice correctly speech
from unknown speaker
in which the s can generate speech with similar voice kind of these big on
we one shows speaker example
and it's not also okay you need are from the combination between the accent and
an arbitrary voice characteristics
however there is another limitation in the current frame
if we only have actually been performed or prompted us to a soft and all
and only is with the big but lost
one the other hand if we only have this data we perform based on the
ideas and only duty as it came across
this is because the publication error for the reconstruction lost to a star is challenging
note that the output of base obviously don't
the house always so improbable
we will discuss our solution to handle back propagation good basically no
the figure shows the speech or with speaker anybody mortars
in the original from all the roles of ideas couldn't we probably conformable why because
of this is in
no postal address the problem is nist meeting gradient of whatever why we try to
estimate the
to understand why the gradient of this operation is not be by considered as a
function
is it can see almost everywhere a small changes in that you would result doesn't
and employed in the all pole
and so on the lda zero
for change very good is not zero the gradient is in pretty
and so it is not used for formant recognition
i don't want lead to good on this problem will be to use a continuous
approximation dataset fashion
but they fail to produce discrete all
so the solution was trained on the two i see our papers which is used
almost all x distribution
it requires a simple mental for all sample from a technical institution we class probabilities
let me talk in more detail
the main problem we use this notion that the calibration is not giving stable
so that all this issue is to use some loss function as an approximation to
one loss function
and three or four efficient way of sampling from the can take the oldest equation
by saving a random variable g to do all the probabilities
and then parameter that controls how closely they used and was approximately screen one vector
instead i equal to zero the softmax computation smoothly to mister are lost and a
simple spectral amplitude of one
on the other hand if the t is equal good vad sample pack become you
pull
the loss is currently in the good over here we place degradation prediction of different
sample y over b y with id two d
in this experiment we use multi-speaker or thing to allow the ascent okay
there is something a bit
with the use of a convolutional next we all been eleven percent relative improvement compared
to our previous frame
"'cause" to somebody we aim was in speech and mechanism by allowing back propagation through
the screen or because they soliciting later
in the future it is necessary to the probability effectiveness of the protocol for is
lined with
no an additional mechanism when we extend of speech into the model chain
we know the in human communication the most common we for human of the comic
at high speeds
but alignment system cannot know what is completely without a connection to the whole by
us to section
so human communication is actually multi sensory and in boston communication channels
not only of three but also fits or channels
humans perceive this multiple source of information together to build the general concept
basically the idea of incorporating visual information for speech processing is not new
we know there is i'm the of is always are
but more samples are usually done by simply concatenating the formation
individually was information can
and this mental usually require all information from different model it is something altogether
but on the other hand but i'll be is often available
we have run the weakness in speech instead of the last three from the mean
of five parallel space and text data
it probably wasn't ability to include a something tedious performance in semi-supervised learning
by following examined it is that it is are given only the accent only speech
data
unfortunately although it removes the requirement of five being for normal apparently a in the
only required to have a lot size of vehicle
so you study is limited only with speech and text for modeling p
so a single before then the fact no obligation is actually working modeled in for
no on the other day system but also results in something
you know proposed multimodal seems jane to meet all around human communication and non-weighted result
modeling
specifically we design a gender detector the clinic speech recognition or a cyber space in
this is or pdas immense cepstral mean or i c emitted by our or image
generation i g
it can be trained using these four points variation by assisting each other given incomplete
data and averaging postal data preparation within the chain
so there is a question now case can we still improve asr even austin's or
text data available
similar to this change them for this case is where we have well i don't
know ready to all speech image and then x
we can separately any start e d s i si and alright g using supervised
learning
next one is that simplifies the mean
in my office emails and text data
the left side is when the input is emails all speech-only data
i is are and i see we generally x this on speed or image in
one
and this is an immense will be reconstruction and allows can be used to back
propagated t v is alright g
come on the right side is when include is text only d o
one and they say that it is and i'm to regenerate sneezing units respectively
and sre i seem to the costs on the text
i this way a star and i c can be propagated to its construction cost
and improve the performance
is the case where only a single node leds available
for example
when we speak only d o
transcribed by asr
and if the hybrid bases are then used to generate an email by nist
and we constantly emails we i see we get another x hypothesis
we did not the laws and improve the unseen more detail
on the other hand when we happen humans only they are i will generate tax
transcription then the caption and synthesise industries by is more detail
and then the synthesized is are then transcribe what is more detail
we then also there is a description against the intermediate extraction
so this is our main interest to see if the image on the data can
help to improve day so
so we can also create another automatically with a single multimodal chain
with we call m c two
because maybe if we want to investigate the possibility of applying the chain mechanism in
a simple remote is of would be more tomorrow
in what you mean and with a process all together with what they are available
we immensely to detect small you know
so what is in ways or speech-only
in speech to text will obviously a emails into x
yes and i d we consider this present in
and the reconstruction was can be used to improve the ideas or i g
and when the in one is that only we can cover the cost function was
for text hypothesis is of immense this the best one two
my back propagated is lost image speech-to-text model can be too
but i that aims at and bt is similar with the one we can the
since pitching
now discuss that's detector of image captioning and image generation
what i see we use an efficient human captioning one real problems show it and
then they'll
and for i t views attention can we just someone this image generation using open
city are lost
well in to the extremes the same source multimodal whatever we to in order
so this in order of seem you know we that is not encoder and the
ica overdone
in the morning on mine the output layer probability for a starting i see in
order to introduce some information sharing
in a single information
well in it was bizarre available really going to use only the corresponding within a
year
one experiment we will probably eight k dataset
eight emails we're in private connection
as for all you use the corresponding sixty five hours not grasp is multi-speaker data
it one by how one and lost
we simulate recognition we're all pretty bad that's not the axes
our okay used to see how robust mental performed in a single one really data
c
so we make the operation you mean subset has different modality
yes portion has pairs mistakes and units
this kind of one also has all model e d but it is a pair
and the one point one on have speech or spontaneity
is the result of our experiment
well the and m c one added it will be small sample currently our baseline
monolingual seventy six point seventy five c are
and with the speech a using a dataset
this eer was reduced to fifteen point
and boston
then by using speech on the humans on t is the are still be with
the reduced to well what is the most expensive
so it is only really a sample be proved even with all speed and takes
the now
we can also see improvement on the other more for example the idea model could
be improved given one of these bits data
a similar and its e
also happened for the m c to a single chain
it is an assistant also successfully we use the z are probably twenty six point
sixty seven
two point seventy two percent
some very nice speech in a lot to train in some be supported by slamming
without a real data
you know we calculate a switching mechanism in the whole be more touching by jointly
training the ica i'd in more detail in a collection
and the resulting feel that the loss to still improve asr in all the image
data is available
okay now all challenge in what's happening unless in space integrator
we discuss therefore we must seen that can listen was speaking
now discuss the second channels
so if you know roles to problem to is how to different agreement on a
side ideas for real-time seamlessly incorporated
we have the same beep or the justice translation table one sets of a sound
and d and d is
in this manner the process of the oscillation is that the sentence by sentence
so forth nice the whole space a difference in the source line with
then we currently in this work into the other languages
and finally synthesized oppose and in the target language industries
used to you meetings the literature to hang on the complete sentence can be long
and complicated p
so most integrator past me maybe coefficient of predatory that's a the incoming speech stream
from the source language to the target languages in real time
for the process so we can come like this
so one point channels for sleeping is the development of incremental asr
and you know we discuss our solution in developing neural increment a star
this can lead to its cost something incremental a size that the one will need
to decide the incremental step and the transcription
the aligned with the conference on speech segment
it's we know the engine make i mean something sequences like when it is not
use of globalisation probability quite the computation of a weighted sum initiation of things i
in plastic we have generally biting or if they
this means the system can only generate x output of these can be entirely input
sequence
consequently utilizing you in situation that require immediate recognition is typical
for storing limit the sri asr have been proposed
one approach is to use a lot of attention
and that and one proposed by wine so
a boy a unique directional antenna with a c t c acoustic model
gently and also what was not only people using broad classes the remote
that incrementally recognize the input speech waveform
however most existing euro isr models utilize from frameworks and learning algorithms
and i'm not completely against you know it is are
here our solution is to employ the original i-th detector
attention with a star which of the sequence
and then we perform i mentioned a where well as a star is to determine
what and highest are yes the students more two
so the isr can be makes the statistics alignment problems based on asr
this is the overall speaker
one is the teacher model which is the non-incremental asr
while the right one is the students more detail which is the incremental asr
and this one is the ancient past four
from the teacher more detail
to the students model
in recognitions their eyes are exactly the same man and four is that i a
summary for the tension alignment wrong non-incrementally sound model
in the last of aligned to the people
to go in and we'll the local and all blocks even more
do not show that performance was it on a dataset
this is the performance of stand out a start by x is the publication
and this is our standard case are
what these results on the result of our claim entirely summer
as you can see that is something field results as well you use the delay
while maintaining comparable performance was final is are
there we d n
summary we give a note that in some other and or you know i-th detector
of neural a star
you know we performed for me
where with three standard a star recipe to model and i is are assisted in
more detail
experimental result you feel the results to really the only
and still active comparable performance this time not a start date with an idea
now discuss how to develop purely limited all previous
so similar to i guess problem
the channels in fact an incremental db is that the model to produce based upon
receiving a call target samples from the and system
as the two handles shortly is then sent bands
we find out dataset by randomly split into four sequence
in short pause and at the beginning and symbols to input your text
here we use different people to differences the human location within the full set comes
so s is the standard speaking and one of "'em"
in the middle center speaking and
recall that we still based on style no
the whole problem and doing this
you know we made from the training in a sentence by sentence of first one
frustration without much modification to the original one
this experiment we used japanese single speaker dataset or to use a data set that
includes about seven thousand and or an hour or the u
spoken by using only the female speaker
the input text consists of forty five one single and then the accent types
a big show the naturalness of the raw score and it's in the size you
in japanese new ideas
we do not use where for that so there has been widely okay between generated
speech and not all speakers
nevertheless here we can see that the size of fa in most quality due to
different unique things
results in it might is one separates or what were
it's almost two point all the more score
and the synthesized speech quality improve for one you in two connecting to or three
units
so that plays an example
the first one is where and the increment also is everyone x and place
you do not match you know that the model that
and this is for to a same faces
you don't have to do not another important
and this is for three i think phrases
you do not much time of day the model
is for the whole sentence you do not to do not imagine
and this is for sentence you do not too much time of day you know
that
this is the clusters any not putting them in and
so the results i just use japanese you know i yes when command files in
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humans can be so the one they speak in real time
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a feedback mechanism
here we propose to also incremental dustin change in which we contact i assign and
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so the aim is to use a group delay and improve my i d as
listening quality by in terms for each other within a short sequence
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and the loss here is calculated for i saw by comparing the i is a
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as you can see we decrease eer baseline really good for seventy percent
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so that one there is something if we don't the ones for more coldly is
the delay and including point four miles of segmental system
okay so now we reduce the overall solution and feature selection
so here we have demonstrated we can also be seen speech in the table two
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variable
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lisa translates the and use that was speaking and template
so this is the situation that we use in this so i
these are our publication data could be still do
yes including nebula since the same framework
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but this is the and all that the eer in some way ration it let
me know by imposing question in the korean in section and you