Neural speech recognition

Tutorials

Dr Yotaro Kubo and Mr Shigeki Karita, Google Research, Japan

i don't everyone sounds fortune this detail this field with for the children session long

nor automatic speech recognition and i not are a global from google research a total

it just started

this sixty minutes be they'll will be organised into boards the fast but will be

written by mean explaining basic formulations and some algorithms from your speech recognition

and the second but well cover software and implementations phone your speech recognition

and this but will be read by my coworker she gave me

it's going to the fast about

after more i want to define what is in your or speech recognition

in decision i used this down for we farting techniques for we are rising and

do and speech recognition but chose techniques sometimes can be also applied to know into

in speech recognition systems

and to and speech recognition is a time for speech recognition that involves neural networks

combining acoustic features directly into words

and you may know already a conventional speech recognizer cost consisting over every three parts

acoustic model pronunciation model and it's more detail

mm on the represents a probabilistic combustion

and this site are wasn't here find the best possible hypothesis from joe smallest

one just a hunt

and two and approach uses systems that this

here and you wanna talk

the diet equal but feature us into procedure is are used to represent forest equation

for speech recognition

well obvious advantage of this approach is

simplistic of the system

it's very make it comes with such algorithms can start in higher internal combustion can

be very complicated doing agreement

within three and two and approach is even extended to directly hunter role will form

signals is that all pre-computed feature vectors

discussion express how to design joe's neural networks that dynasty outputs words wrong feature with

the or role of all signals

easy as in the in this fast but iris brand three approaches for and speech

recognition and

also recent advances over chose three

it's called is a fast section

most of classical speech recognition models use this integration unit score

because the generative story or feature vector sequence x

and a procedure as well i

and b models the distribution of joel two variables by introducing

as shown to latent variables

so phonemes cd as well in here and the related hmms to she guessed s

usually be decomposed into by assuming that phoneme z yes

is generated depending on the word

and an si hmm states are generated depending on phoneme sequences

and features that yes

is depending on the agent states

so here me to carry assume draws independency assumption between introduced variables

yes in this assumption looks okay but yes in section will result in some languages

in conventional approach driving techniques introduced in each component of this decomposition

for example fold and that's what are we often used

i ran in and it's model here for getting better prediction all words marshy genesis

well as of what acoustic monitoring people when used t and even your network or

a recurrent neural network for

one thing this emission probability of features you guess is

in the next size i review joe's or rolled used to enhance components with it

writing techniques

t n and german hybrid approaches are very famous way to enhance the

conventional acoustic models

in this approach this definition the emission probability it used as an acoustic model of

the conventional speech recognition

she a the probability p java that even the hmms the there is transformed into

a probability that is proportional to this special

this is the ratio between the pretty if a probability of all agents today the

given the feature vector and some as not probability all the agents date

the predictive distribution is modeled by a neural net and the marginal distribution is modeled

by a margin on the other wall categorical distribution

this is a convenient way to bring expression ability of neural nets into

the conventional speech recognizers however

this have similar programs actually

cost

be used as division in you and then to permit parameter we use marginal distribution

independently parameterized by different parameters

so baseball's used here is just an approximation because the different modeling parameters used for

the marginal probability and predictive probability

secondary

it is known that a gmm stay there is a very difficult times it's been

be to estimate it

a classifier was yours

classifiers i mean you're metaquest for us

cost a for example some stationary bothers

is very difficult to classify the acoustic feature vector with a is belongs to the

fast all the phonemes segment was a second how all the phoneme segment

this fact makes training and prediction of the classifier more confusing what a stable in

other words

connectionist temporal classification can be regarded as a remedy for the

that program here

is easy more than each time today where is represented only by a few points

in the c yes

is done by introducing tommy a view here we according to brown

and associate most all input vectors to the rank k

only few input frames that i kind of center over poignant continuous to the final

output

this diagram shows

the speech to or your network this easy approach in this case is

when we have infancy yes with eight elements

each in the to be that is classified into name is augmented with the banks

in more

and the final result is defined by removing banks imports from the output

one advantage of this you want it is that be no longer i'm used to

estimate a gmm is data davis with using commission a speech recognition systems

so it is possible to train neural networks from scratch

also dct is in your in it is

jerry that we can use eight four laboratory see us to seek yes task encoding

and in speech recognition

so it can be used either to estimate phonemes you can write conveys no religious

order to estimate or she can or grapheme cts data into and approaches

however each day the here is estimated independently so there's not able to david dependency

it's a and b elaborate on the didn't is the induced by c d c

it is known that run a session one graphically move or in c d c

is ensuring be written represented by finite state transducer

if we present it in transducers be seen that the conventional left-to-right hmms and c

d's in your minutes

have a quite similar event is used for your

so in fact using only c d z for speech recognition is

in fact very similar to doing speech recognition results using language models

however still see it is you have some good properties

well the is it

in that

it can perform better combination with down sampling approaches in neural networks

commissioner broad needed

gmm based i meant that doesn't work very where with down sampled features

also even after obtaining an hmm state alignments the conversion i'm chinese to associate single

related to each time star

that makes the a very information or on the bus in the regional planning boundaries

and this ambiguity becomes more if we if the feature is downsampled

so it is you only classifiers

some kind of center of segments so we apply this i'm bus today is

related to that the second advantage is that we don't need to classify some phonemes

structure

nice the fast and second how full bottle

this makes training was terrible and also prediction more complicated

that means that it is combined with some such are voice and sid using neural

nets tends to make score defined as roger for each examples

so using cd see for classical speech you speech recognition is a good idea because

it needs down dating wanted to within the labels

event is e

even if c t z is used as a part of the system we still

have advantages described before

so

don't somewhere each and every be applied and also it can form a good combination

with that such algorithm

is brought presented by stack

so our indiana well there are eight cars of commerce now hybrid approach is unseat

is the approach

this is that c disease either want a just also vocal tract in conventional is

our systems

it doesn't next component

now there's more less channel be enhanced by introducing are enhanced recurrent neural nets what

is the atoms

long short time a more in your on its base order regression inputs

are in a language model by the x

this division over the next word by r antennas

that ingested always afraid guess boards

unlike previous n-gram round is more approaches are and then someone i did a word

and its context in a continuous vector

and use it to make a prediction the next work

since we used a reference for making dis-continuous context you please in addition

irina spanish monitors channel in theory hunting

and no infinite drinks of our history

even so in practice it often very difficult to optimize someone or in that very

nice to see significant improvements from n-gram language models

as a downside context representation are analyzed models i e

in n-gram approaches

the number of possible context is bounded by the number of different war history that

is finite

however a four hour and if you wanna be do not

do not used extending over the context to be used

so each different work is to have the defining context a good representation

one can say

this is issues downside for computation

but in fact it's not that inefficient

is very easy this idea is models

this going to presentation to carry you guys should space to store in memory wiring

harness was somewhat something

maybe compare the size of speech recognition systems with a conventional approach and free neural

network approach the size is actually compare or and your and it's are you was

more as on the tree expand it

a weighted finite state transducers

so it might be a bit counterintuitive button urinal neural net approaches actually fit very

well with

mobile devices to

it's very if the device has a some accelerators full matrix multiplication well example

another important property that inference is the competition or if you change is to organization

she's done in conventional approaches use

takes the rents context for making a prediction each part of token used to be

long enough for making i'd rate reduction

however irina stand out from when the context

that means that we can use finite organization metal that is some word tokens well

maybe we can use a grapheme based or close to

to document organisers used reason you unacknowledged monitors

most are very similar in the in the sense that talk as all the data

by matching existing control and the algorithms that these chaps database tokens

and they gradually margins in

both select pair or tokens marks might in some criteria

but encoding pde use these

the number i just and occurrences of tokens in the dataset whereas

work this approach evaluate the likelihood well what dataset we do things simply not models

over defined tokens

using the draws final vocals decoding result in a smaller tokens that

and the number with different tokens

in the system is often corresponds to the size of out three open your networks

thus

it also contributes to the computational efficiency of neural nets

now who introduced in additional c disease and advantages of around the dance

the distinction is about hiring transduced us that can one strings or bottom results

as i mentioned she did she turned out to be sensitive and it should be

doing output tokens

i don't and channel be used as a component that inject the household event is

a so

by combining cd z based prediction with are in an n-best contest hundred we get

are and transducer

this diagram shows the

the as texture or are in a transducers

this thought of as a director

corresponds to c t z predictor

despite compares distribution over the nist tokens

we have the tokens it is all demanded by all made it with a down

symbol

and this but correspond to our own in and

this of feedback loop next the prediction to be dependent to the previous words this

actually inject the dependence you to the previous of talkers

c d c and r and d is yes us a common structure that use

rank to and the input and output elements

as i shows in the cities each s it is it a free corresponding to

the

hmm states in the conventional acoustic model

and a similar to the agents days it is handled as a latent variable in

the likelihood function

as you are

this latent variable is marginalised out

two defines a likelihood function and a logistic function

here

or c d c and a rarity models with brock symbol use this

simple handcrafted model for probability old wires regions given the alignment c guess one

due to this simple definition of the probability all by

given by brian

the likelihood function can be simplified in this way

difference between c d c and r n and t appears in the second component

probability all i meant

given the input feature vectors here x

c vc introduces frame wise independency here we identity introduces the and i'm in predictions

that is depending on the previous i meant variables

to explain how i'm it is more the reading and t is process shows the

case that be how for input vectors

e one e to easily and e full and really fast or the u s

c yes

low and word

we show the case when the difference was a fixed as in the training phase

i'll original joint network denoted as if here

it has defined by the corresponding to different times stand for the other thing that

and

evaluated things of the context in your handling

to fast estimation is given by feeding the fast in court

eva and initial context here she's there to the joint network

if we close to that the fast output of the model to be block back

need to be finished reading from the current encode either

so the more the start switching that i two

if the second element of the i'm and see us to be the fast talking

in the reference

that is he huh

it changes the context with stuff from c zero to see one

and

the model continues to pretty if the nist of but should be back why should

be some other words

for example

if the past that outputs is to control can low is chosen

so context mister will be changed from semen to see two

by repeating the same process until we reached as a final step here

we get the posterior annotation knows single alignment cost

for the training also neural networks we they didn't diamond variables

we need to compute and expectation of agrarian visitors with given the alignment variable

well as the posterior distribution of the alignment whatever's here

and study

for what are wasn't is

used for this purpose

how we have a for a lot colour wasn't although generate graph is not computationally

efficient

to say it's not

g u r t but for entry

however i meant defined in are in energy in bright is good it's shaped event

is you structure

for this kind of stress enough to read "'em" problem for what i wouldn't sufficiently

fast to be some can be you or gpu accelerate arts

in this case we need to compute the sum of probability or what for the

past

generally you know them us or a rose

and the prior probability that is a sum well probabilities

wow colour cost in order to buy greene story are hours

since well as summation term be written as

operations annex sifting and summation is done be efficiently implemented to be t b you

for example

i know i'll try to introduce encoder decoder neural networks enhanced with attention recognition

c d c and r and d house i'm and variables to actually this size

to encode out to be if that shouldn't be used for making prediction of the

next token

this kind of information is all formally five us attention

if the point is about estimating we have to

we've got

we don't models of probabilities division one that times time varying we're directory that these

where

i is the timestamp we should regard for making prediction for ice world

we can construct is by using softmax at a young with in that computed from

the input see gen x on the previous two words why well do i minus

one

we combined attention probability into simpler are an n-best encode the and are in like

decoder

this is inspired neural networks defined

that is

we introduce addition one true

task it's the information from or encode all the and the decoder thus there a

state of the previous time stuff

this internal computed a

tension probability

i mentioned before people given

p o a

given the context and go the outputs

and in this module outputs a summary big summary bit the by comparing this expectation

the addition probability introduce the here is typically defined

by introducing a function that you believe then smudging score was similarity be doing decoder

context information and the encoder output

that is the t-norm as well as a here

if you have this a be represented by in your pet

all the components including composition of expectation one this probability distribution function can be optimized

by us improve about repetition for minimizing cross entropy criterion

compared to a rarity alignments here is internally represented in neural net where are energy

handle it as a latent variable in likelihood function that is actually objective function to

this is of course of attention right soft adaptation since we already used in court

output via and expectation as a relative prediction is made after deciding feature quote unquote

output to be used

so foundation is better in terms of a simple still be implementation around the also

optimisation

and it's also vegas that it has no few

it has only few wanting assumptions

however a combat the identity it's harder to enforce monotonicity of alignment

in speech recognition

same as well and corresponding of acoustic features assumed to be in the same order

we assumed that additional should be

monotonic

if we if we brought addition probability like this problem where y-axis is a tradition

in the right of tokens each and x-axis is a rotational in the encoded feature

sequence

the most probably most probability mass should be on the diagonal region

however us as soft adaptation is to flexible we sometimes see of diagonal peaks that

these

we decoding is more data for resolving such programs

well known to work extension force of traditional roles itself attention on transform us

okay jamaican can be viewed as a achieve area store where curry is computed from

the decoder state and itchy and variance is

but i are computed from the encoder output

so far addition is an additional attention components are computed everything queries cheese and of

various from the previously as output

a frisbee speaking this corresponds to g attention to the input from as a time

stamps

and z is of great human to joe's adaptation is also computed based on the

previously you out

transform is a neural net component activities this separation the us multiple times to integrate

information from

in that i as the timestamps

we just construct

both encoder and decoder based on this transform or

okay very transformers and nowadays used as a drawing you go is made of our

antennas

so we can use it for constructing acoustic model for almost a hybrid speech recognizers

or region defined transform a transducer we have transform a is used is that all

are in it or are introduced us

the last section of this but is for introducing within the elements is in your

speech recognition

even so and the in speech recognition and its related technologies in disagreeing with it

missed you how we have this element it is compared to the conventional speech recognizers

i will focus on the united disadvantages

the first one is that with the conventional system is very easy to integrate side

information to bias the recognition result

and the four and architecture is not trivial to do so

the second point is that into and speech recognizers in general requires huge amount of

training data to make it work

so in this in a method to overcome data sparsity issue is also important

the starting point is that in conventional system it's relatively easy to use compares it

does such as text data or no transcribed audio data

in this section i various miss some examples all studies

for all welcoming joe's conditions

possibly is about biasing results

by things is particularly important for real applications

speech recognition all used to find something in the database for example if we want

to build a system to make a phone call

speech recognizers shows a button name in the user's context are used

same kind of behaviour is needed for various kinds of entire eighties

like sometimes or what names

in commissioner is biasing speech recognizer is very easy it can be done just by

integrating additional language models that has enhanced

probability for such but in cities

well solution for this into and rows is introducing another addition we can see that

focuses on

predefined set or context vectors

i we explain the middle of cortical texture us one text out this the utterance

where

in this method context for at such as a names or sometimes i encoded to

single vector

on the other jamaican detect pitch context of it does should be activated to the

court to estimate the next word

and just an example were normalization probabilities

well as it out that

talk to

is addition we can start to think that some biasing for it is like but

fruit are you all want to brew joe's actually corresponding to some names

and this additional input vector representing context

is expected to have the rest of the decoding process

so after saying after the user saying talk to it is expected that some i

can imbue for all

and this context is attention mechanism can

so we still behave via by a by adding additional probability to joe's a name

context against us

the next topic is about marriage would get a model for welcome data sparsity i

will introduce a method proposed by d

dismissal is simple

that just a i-vector model vector representing dialect as an input

and use that it does that constructed by pooling the data in all the dialects

if we do have decided to dialect id in but consistent during training and decoding

speech recognizer trained in this may cancel each some more

depending on these input data is dialect

is a multi rate

from this role showing the base turns out it's

we see that just training into in speech recognizer result in stairways mass there does

it is not a good idea of the performance significantly worse in dialects with smaller

datasets

this will shows the result with transfer learning here transfer and you fast

the miss out that fast that price training will include it does it

and then applies the oppressed training on the matched to dataset

transfer aligning thickening actually improve the result

however we could all the dating further improvement just by integrating that is a dialect

id in

including the previous method i explained

before contextual a s having additional method in time that is have people were coming

knuckle dataset

so sitting in your architecture that can probably handle such additional metadata in but is

in the important nowadays

the last the is about the musical on data

as i have already mentioned and speech recognition "'cause" huge amount of training data

and is even worse because it's not true or how to use their data

conventional speech recognition can be found at least privilege test only data for language modeling

and also it's relatively easy to use a possible by mit line in the top

only one data

overcoming these issues of the bubble retraining is no again getting four

here

we want to optimize encoder all speech recognizer only by using non transcribed data

of course it is not possible to powerful cross entropy pruning or was the neighbours

if we the if the data is not transcribed

inspired by bottom involved in that are not image processing field

within the missiles use richer information to be context information on the instantaneous information

mutual information is engine there are very difficult to optimize but recent middle we are

as it by drawing

the missile correlates contrastive estimation

in this i want to explain the famous network called we have developed to one

or

this is a diagram for the wave double two point one you're

this mental is aiming a pre-training all she nn based in quarter by maximizing mutual

information between input outputs

and its surrounding context

context surrounding context is actually summarized by a random transformers

we baseline want to maximize

in formulation of infancy we describe want to maximize similarity between projected in order out

on context vector

are we have a is not assumption if we only do that similarity between

and what i'll put on qantas with the because

the similarity becomes maximum maybe enhance the in order that matt all the data points

into a single course of what's that one zero vector

in fine is the introduces another somewhere here in all the all split from random

times files

and in fantasy tries to minimize similarity between context and random resampling in order

so this famous so that we can maximise you know maybe doing contents untied in

all the all but

but

it minimizes melodically with the mean the

context and randomly sample in without

we have the victim point well it's very famous because of its surprising performance speech

recognition problems

it is reporting that only few minutes of training data that is option for i

mean and in speech recognizers if the encoder is trained with

well that was fifty thousand hours of training it contrast them training

so this amazon want right plots from training data is actually shows but it should

be we have three year old a need combatted to utterance to data

okay and you minimize for watching these but is it for my part

then it will be the best you key and then this but we have you

about

software aspects or and in speech you've right

probably rate on this is typically from google research that's okay so you implementation for

a total and eurospeech question

today for talk about the two kids well what you're in five minutes

and then

we will try pretty doing model was in the toolkit

introducing the protection

after that we'll trained you

neural speech recognition to model from score parts and ten minutes

so far and we are we show how to extend the money out and tasks

introduced in your little section for example how to the sorry the transform of knowing

state-of-the-art and or something like that

so we'll forcible i'm sure that to locate all of you

this table is

introducing

you mean magnets

a c l paper

this table briefly summarize the

kind of comparison between the various to the kids

in this table all the

posted to okay supports the

automatic speech recognition tasks

and

some of them

also supports the

different tasks like speech transformation on the central station

and text-to-speech test

and

note that there is

pre-trained models are available in several to get

so

in this tutorial

we will focus on the svm

because it's doubles many

tasks

for as the and two in modeling

and also it's of boats to train the model

so i think it is easy to

try

its implementation can is host it at peak at

and if you want to know more digit result

they are described in this paper this paper was

no is a speech recognition and text to speech

speech on section reports all the obvious on the part of the

news speech enhancement

feature we will be coming soon so respect that rate

in this to treat you know

we have try yes mean of two

it is kind of major update from the yes one okay there is

so there are differences

in the between them but measure origins

for example

if you using it is to depends upon any primaries for example county is to

get sent a however

used to taste minimalist approach

it mainly depends on title ish and it all from we can use integrate it

scully

and the world model

almost same

especially tts models more used in a long

and however this tuple the task is

kindly well in progress

however

this meant to all visible once it's all so it is nice to try if

you're interested in itself on tts

and also speech enhancement previous

if you into a sitting yes one please visit this you all out

it is show you the usage of the use of long

there was to use the speech tutorial

and this tutorial have long ago example posted not go crawl

good across from a base

pricing to print the in a web page

and you just can't to also samples to a after a to so

but is make sure that you are using could one time in court probable

by this thing

when you visit this very page because the one of them called

we used in this tutorial

this just the introduces

pre-training model

that means

the models or really train by

one on and some tasks and dataset

yes in until

the such and models

in

yes peanut models to report three

and hosted that senator

for example thing as all task there is

they're already speech and a mistake for english speech recognition

and t s j for japanese

so a score young for very on and so long

and tts have

also have already model

there

if we wanna

see the fruitless angle of the a novel

pretty c this you although

this

cindy s shows the how to use that

in python

for two right so

we have performed the

not controlled

to ignore the checkpoint for channel though

and i'm fact that to do this model object

after that

well you can believe that

some we wait for on

in you will call environment and its transcribed the result

to do this results

now so rats

get started and crawl

so basically the you all out in the page eight

you will find

e

note of it

like this

therefore trying we will

in still use

and before

running at feast make sure you all collecting

the i could a long time

it is

available

on

right corner

and priest select the change runtime five and

check the gpu we selected

note that the u is not

what it and see if you might be

so you want the training

so forth trying to instill using it because it is not default to install just

be at u

in a single core

so i can see if you press how many dependencies

because you can still for both you can't one used to

so or s

provide a pre-training model

so

first

i downloaded the waveform file for them

i resist this dataset

and i try to

for phone to is not all

on the

downloaded waving

so that before this

forced to right

you download that a pre-training what we'll

for example this mateo is trained by stingy button okay

using the unlabeled speech

yes all task

and he seems to you is to transform a picture

for neural networks

and

i think roll the waveform here and feed into a more below albeit

and that's right there

i'll but is a and the best result so well i selected the best one

to see how it looks like

so this is the result probably read speech model

sound check the

but the wave onset separation just starts

since i

false pretty well

so let's go back to the slide

just after we're show you how to use so for the wrong defined tasks

testing it directory your is the x two

that contains a although it so that sets inside that

and you five and the static content with same fires on directories

right column the yes onto

you basically you're on this says created from the cell

you produce the results reported in this really mean file

so i'll show you do well

kind of stage is inside the we want to sell you can start point two

stages or of people

but in the us stages

a score

specify the command drawing for box

one to five state it is

perform data preparation and six to eight for as long as model training and ninety

temporal bones is all training and after that the sre variation be performed

and very you got brought to you or entering the more used to put into

so that's need of it is of the data preparation stages

in just a very all we're focus on and four task

that is very small right images nice to come from i

for fast experiment

and the for a very fast daisy performance the positive and then data before reaching

utilize it is the task and then fires at the other everything and four but

it into the cup of these style that and after that we performed some preprocessing

the speech and text is it

as

value that was set in

the case so i a we use the you dior sentence please a lot of

to the text representation

so that representation we used in the training and evaluation stages

the six to a stages we performed a long as model training and intermediate a

very efficient like a public key and after that the itself training and decoding and

evaluation is performed

so you can

one of the training

the board using the purpose of or even go control it is okay

so you can monitor the log of the of the softmax output over a wide

or something alright though the c g c out of it can be morning to

during training

and this is a example the is it or you corporations scoring results looks like

these s a wide full

very efficient tool and reformatting results with the amount that because it it's more readable

and as you can see here for each opposable error rate and also something that

both the cup of the right was talking or rate

and finally we have had to train the model and you can use to exactly

saying you maybe i'd draw it is out inference you think more using a v

i

like okay i'm in the results in the beginning

if you specified

but kind of confusion six point two you use

so now it's got to the court

no way not to the controller

so

let's see the how example two directives like

okay you can

used

come on the right thing

like usual not work and you can also use the file explore from

this icon

and you define a many

datasets are available on the is to and

in this study we focus on and for all and decision is all one task

and

for now we are on london style

in just

israel

so

before are running the associated to any more but dependencies

two one training

a carry enough always

you quiet currently unfortunately so we needed are all the pretty complies

to use and also we need from

binary whom have to get and after install everything you're the you're on the on

to sell

here

so

for star

the

that is you down and of all four from cmu store because it is really

of a novel so after the enrollment is substituted the data preparation movie again

and

you can see here there is a menu will finds the and data training is

performed and the state five spoken addition

or text that cystic cooperation really figure ten

and this five results of this from the set s ps

so yes

and a for a few used to the sentence piece as a focalization

and after the center this

training is finished

the target money would be retrained

let's see here

and after that the sound training here starting

however i drafted to use of training because it if you're wrong

i finished this

training and ten minutes and i think it is reasonable

so let's see how the video data looks like the but that is distorting down

and we can find some

we have a

prepared it down here like a this is the internet or out with the text

file is here the fast

and three shows that false id and you will find

you find the corresponding speech in this while the nist p five p so if

you for ages from by you hear yes i is that we have some t

a

so after the

training was going to train the

speed you have to be used as the

blocking dft of the training phase and screen

it will store many things for example pickle

five some of detector the checkpoint

here

and also attention is wrong addition we have used to a here and

configuration can be accommodated according to the animal

and let's see you how the location on the looks like

so

configuration you are provide everything

every information in during training

so here is kind of

result of the cup operations so for example you to use is five point zero

is that we probably integrating into a non party and

you to use is this kind of like this result

usually it's like the binary to use this in this piece

and used our in an that's dying graph structure

okay

and

during training you can or someone that the pencil or

inside a good record

or you are or environment

and you go far in your exact after operatable it is so severe and achieves

icsi

parsons partition

then right

it is the output

so that it is only nice so yes see the other information so this is

there some visualization is i x d dft

as seen the voice very short utterance so that i and does not

we really five

additional dirichlet allocation right and i think

it's okay

so there is a variation result

and

but i said

the last it is for more details on down so i just pasted to the

e

not sell and

you just here the final result of the well there are and it's starting point

five in the test that i think

i mean i soundtrack the right is sixty four point nine and but can write

the six point five

okay so that this

you lose the

so one at this for infants at i

so

first of all we need to specify the fits point to use i document to

use this

very dark this because at it seems to be best

two point eight or so

we then use that the result really

according to have the same as the speech but it looks

more than seriously speaker that is it is more

okay so

thanks for putting the stuff there

this that stuff there will explain how to extend models and pat task

so that's

the total section in

he interest to

and cortical architecture and transparent and our transducer

when you have regression

there are they how to use that

it's

this is the answer

sometimes

like i and four task deftly already says of the predefined

plot configuration younger fought so you can just

that's fine why is a coefficient and take a look at that you going and

there are none of the values of a number of the units

inside younger five

i think it mostly goal of this fine is that yes it has test trying

many things like activation or

where tight so

make things like that

however if you

down some find that you can extend multi i think i said

for example

the

or and then what or transducer encoder and decoder but works in a men's these

interfaces

to ease the swat four

have keep the complexity between those variants for brain

implementation

so

this

and e s

other so i used to model

for vice versa yes feature plus four plus a

these two

others and go the invitation

for passing the encoder speakers and text input on the targets to i'll stick to

the

something like

as

explaining that

you're though it's

figure

and you can use that phone come on the right argument if you this is

just a you implementation in this

so score

and

if you want to send your task like you wanna

try sub tasks you on the is that are it is well for possible

then you extend that i was task

so existing asr was tedious task implements this

that is

and

to get the this

task i don't think feature

like a distributed training on divan sampling but checkpoint rejoining like that

as the was gonna section five we show you how used in payments

that

models

so that is it yes did have rivets e

and that and check the yes to implementation and

okay

the out into for some so

and there is

model definition here

so as i said in the us by a base

it implements have a sort the svm modeling the phase here

and actually simply call use the board mess of

the read and the most value is

so received a for the nist

it's here

so increase to use this be used in baton text output as seen that argument

and then it we kinda rate and was fine tuning full

euros the angle tunnels

so well that's in there

the first thing go the network coding rates the without the input of the think

of the networks

still this angle regularization and

well you see that output and it and

this is the outfit a within good as input and

they're pretty they're

text target

and calculated function here and the same their thing having in thus it is inference

so this is exactly same impotent target as well as the political there that those

are anti do the same thing

yes exactly same arguments

and then combine

thus values i-th honours the scrolling nazi it's quite easy and

same as the so we into using their you know section

so

thanks so or watching this