0:00:15however while missus and shall show from actually known anonymous the whole thing of all
0:00:23i like to three then my paper name a personalised the singing wise generation new
0:00:29thing one five and
0:00:32a saliva local as individuals not basic idea of singing wise generation
0:00:38ran about the related work and the limitations
0:00:43i know how that the proposed model and it's quite well without
0:00:47and experiments
0:00:50so stimuli stun dimension is actually technique to train or anything new
0:00:55for you the remote call-in to lost then the ten placing me
0:01:00and the
0:01:01after gain in this to include
0:01:04we will
0:01:05that is a singing out to also you there
0:01:09from this thing wise generation based on
0:01:13all this task is actually a challenging
0:01:16because the generative is singing
0:01:19should be as an actual has thus and everything and i and also need to
0:01:23all of them ten
0:01:24the thing and bowl and we've done or templates
0:01:27and need to be similar to the you know wise
0:01:33this one
0:01:34it was a after is different from and ten placing a
0:01:38so all one way
0:01:40class analyses fusion
0:01:42phone usually transformation and the synthesis
0:01:45or this task
0:01:48and there are some related to the task
0:01:52just one is the speech to singing collection and also perform and lastly
0:01:57it should transformation and that since there is
0:02:00as a solution
0:02:03but difference here is the input is the speech
0:02:09well actually it's a lot of the thing content of course training
0:02:13for then
0:02:14for you e
0:02:16you're the speech my heart will go on
0:02:19will be bozos this person's
0:02:22my heart we all on
0:02:24and this speech was in equation purely rely on a
0:02:29speech to sing
0:02:32and the parallel speech to speech and singing
0:02:36they got
0:02:38but this is also low from real well for particular
0:02:42we will within generation
0:02:49well task is the singing wise convolution which can also generate
0:02:55well this is
0:02:58that's to come were sourced in seen as was to talk case in this one
0:03:04and there are two basic approach first one is the long run parallel screen okay
0:03:11which means that they have solved and have a stinging
0:03:14and the two
0:03:16a speech analyses
0:03:19and the same face
0:03:21well we'll can get nothing about
0:03:24and second one is the real the ground parallel screening eight
0:03:29but you really the time t
0:03:32speaker identity what is this one need to be learned to the conversion model
0:03:40different have a speaker to learn a no need to be trained
0:03:46well as on the limitation in here is
0:03:49for the first approach
0:03:50then you
0:03:51i mean alignment for second approach
0:03:54then you to retrain for different target speakers
0:03:59then weight control singing was generation right
0:04:03this applies the
0:04:05and i left and commercial model
0:04:11the weight for a walk over the whole single noise generation
0:04:16the training will be
0:04:18two steps
0:04:19first one is the right list and model training
0:04:24what is actually true can work speech i-vectors
0:04:29same you
0:04:30g p g r zero one at
0:04:32to singing and theses
0:04:34then mm
0:04:35seconds that training is to
0:04:39converting the speech
0:04:42saying you have zero mean he
0:04:44and m c to singing wise
0:04:47what is the
0:04:50the ural a way to
0:04:53condition only part and
0:04:57so you way well assuming we have will
0:05:01parallel to be shown a singing
0:05:03well training set
0:05:06one can performance to training procedures
0:05:10i-vector n is still
0:05:13to clean and that a speaker identity
0:05:16s zero at
0:05:18if the prosody interest to well
0:05:21the from time at a ten placing e
0:05:24and you you're a is the speaker independent eature
0:05:31so for this right
0:05:32at runtime we will have full
0:05:35a time t speech content a singing this song to cause a
0:05:41the ten placing is all your liaison
0:05:44professionals be necessary
0:05:46really hopefully can have low professional seen as
0:05:53prosody and ten
0:05:54ten whole
0:05:56we will well
0:05:57have the f zero and h e
0:05:59and the purity
0:06:01from no
0:06:03there again have little i-vector
0:06:06from speech
0:06:07why probabilistically include to the
0:06:10training now rest model
0:06:12we will have no convert a the
0:06:15and ceases
0:06:16well then
0:06:18we'll still have i-vector and see near zero she
0:06:23to include the training that we are and what order
0:06:27and this will all there will generate a final
0:06:31a little thingy
0:06:32silencing me hope to be well
0:06:36the same
0:06:39speaker without any speech
0:06:42way so
0:06:43ten minutes in the
0:06:45sings that'll
0:06:47i q it is still
0:06:49one problem
0:06:50for the pipeline
0:06:52what is done mismatch between training and testing
0:06:58for which are and we'll codec training
0:07:00that includes features into a low vocal there
0:07:03s actually
0:07:05an actual and
0:07:06and c is it is natural and it is extracted from actual singing
0:07:12but at a restaurant and conversion
0:07:15here is a commodity and this is from now it model and this converted and
0:07:22well beyond be different
0:07:24from the natural and disease
0:07:27so this
0:07:32you know the channel right okay that's being killed
0:07:37in order to overcome the mismatch these two
0:07:41we propose low
0:07:44into quality network
0:07:46then this network is to me
0:07:50evangelising we are
0:07:54conversion and a low coding together
0:07:57cool basically i
0:07:59the training will be
0:08:00not single
0:08:02and only one is that
0:08:04which is to
0:08:07speaker identity
0:08:09from speech
0:08:10what she's i-vector
0:08:11and the poles of the
0:08:14and the linguistic or present representation under temp placing
0:08:20to train the way for an
0:08:22channel right
0:08:23senior a tote
0:08:25directly so at runtime
0:08:28we will again have will
0:08:30you'll this being each
0:08:32to extract no
0:08:34you'll thus
0:08:34i i-vector
0:08:36and then we'll has another person's trying to say now
0:08:39so the time placing mean
0:08:41and we will again how the
0:08:44prosody much as f zero at a g
0:08:47and the t v g from the training
0:08:49then one for this three feet ratings for the training
0:08:54it's not one will be lost
0:08:56but are generated a singing
0:08:59and this year
0:09:01there are we would do not have no
0:09:03when converting that i'm c and then actual and sitting mismatch problem
0:09:10way about
0:09:12since the size
0:09:13then the optimal
0:09:14okay we'll be included
0:09:18that's as a result
0:09:21for the experimental way
0:09:23we experimented with two database
0:09:26and the model
0:09:28based testing also
0:09:31speakers voiced concerns
0:09:33and of interest
0:09:34was extracted from
0:09:36what worked on a
0:09:38followed by s g u i
0:09:40and other allies and model
0:09:43was performed on modeling truncated
0:09:47we will welcome past three model the first one is a path i'm way because
0:09:52is cost
0:09:54and that this one is the one we proposed
0:09:57the second one is the
0:09:59okay different
0:10:01clusters one
0:10:02what is that i
0:10:03we have the l s can conversion model like first one
0:10:07and down way
0:10:09i have no word one of the in an all pole but our algorithm where
0:10:13you was lower than the here
0:10:15so long you press
0:10:18the first one is a label and will call the second one is what marco
0:10:23down way
0:10:25you why the weight i one without
0:10:28i to the evaluation approach case first one is an object in the evaluation
0:10:35second one in the
0:10:36subject to continue evaluation so for
0:10:40objective so you one iteration
0:10:42we can form the root mean square and roll
0:10:46this is to measure the
0:10:49distortion it and that have a singing and of the current work teasingly
0:10:54i the low
0:10:55so an election year
0:10:57well which
0:10:58actually means
0:11:00well the lower
0:11:01the l i cu
0:11:04but you will need to cater the similarity scores
0:11:08so why you well wait i was really system
0:11:12where are and
0:11:14and c
0:11:16so laces can
0:11:18the fact i
0:11:20what is our crumples integrate a one
0:11:24we can say our integrate it model outperformed the past i model
0:11:31and though when they this
0:11:34actually means
0:11:35our composed model has radios long mismatch
0:11:41and i think it in turn made a features
0:11:44converting and c and a natural and see
0:11:48so that we can get better results
0:11:51our a modal
0:11:53propose a novel best not all forming
0:11:57no along with the word will go there
0:12:00all which we also found
0:12:03man a similar situations
0:12:07even wise conversion
0:12:09right along all the
0:12:10can be better than one e r vocal the sometimes
0:12:16all objectives evaluations
0:12:22so forth if a new regulation
0:12:25the way you evaluate
0:12:28all closing in quality and analyzing
0:12:32so way actually away
0:12:35telephone the listening test
0:12:38well for all of the comedy essentials
0:12:41and the whole system way
0:12:44randomly selecting the utterance
0:12:47and l
0:12:50a selflessness
0:12:52but is encased being the
0:12:53as an intact
0:12:55way from a unique referenced asked to you anyway so only
0:13:01and that x a b and asked to leave anyway though
0:13:04and are added
0:13:06well first
0:13:07our proposed model way the
0:13:10a model way so
0:13:12baseline model way somewhere the one over there
0:13:15so of the yellow one in our proposed model
0:13:19why is the baseline
0:13:21we can say i work of course not all on the basic tagging time so
0:13:26all quality this is a unique reference task
0:13:29and similarity
0:13:32this is an a b preference test
0:13:35and a full
0:13:38and on the
0:13:41i'm terrible
0:13:42samples model and the pad thai
0:13:45we can also
0:13:46although there with us in the trend our proposed a novel
0:13:51awful form
0:13:52the all data and a low internal
0:13:55in comes all
0:13:57generic and only
0:13:59so this
0:14:00significant improvement a unique at our proposed model has
0:14:09has some
0:14:12from the by far the integrating
0:14:17i also plays an animal here
0:14:22two and a half an hour components model
0:14:27okay result okay speech
0:14:35and the knowledge that anything
0:14:45is the
0:14:46we had time
0:14:54is the
0:14:55propose one
0:15:02well another baseline we with that our proposed model we can hear
0:15:11and that and within
0:15:26and our proposed one
0:15:36okay here an optimal for you feel like in this website
0:15:41and i would like to come the low
0:15:43no this paper so our proposed model
0:15:47actually does not require hernault thinking they have more training work anymore
0:15:52i'm wondering system
0:15:54and then we also do not
0:15:55need to train
0:15:57different models for have a training
0:16:00and although there is no frame alignment needing us critics
0:16:05well so on what role speech and mismatch you between we are training and drawn
0:16:11from which are which implies better quality in there are people who
0:16:17and then the experimental results also i already have in this all the proposed modeling
0:16:24handle both
0:16:26well quality and other thing that are
0:16:29and real-time you feel have i mean and that's
0:16:32an email me
0:16:35and you