you've now learned so much about deep
learning and sequence models that we can
actually describe a trigger word system
quite simply just on one slide as you
see in this video but when the rise of
speech recognition have been more and
more devices you can wake up with your
voice and those are sometimes called
trigger word detection systems so let's
see how you can build a trigger word
system examples of triggering systems
include Amazon echo which is broken out
what that word elixir
the by dou dou R or s part devices woken
up with face duty how Apple Siri working
out with hey Siri and Google home woken
up with ok Google so stands the trigger
word detection that if you have say an
Amazon echo in your living room you can
walk the living room and just say Alexa
what time is it and have it wake up I'll
be triggered by the words of exer and
answer your voice query so if you can
build a trigger word detection system
maybe you can make your computer do
something by telling it computer
activate one of my friends also works on
turning on an offer particular lamp
using a trigger word kind of as a fun
project but what I want to show you is
how you can build a trigger word
detection system now the trigger word
detection literature is still evolving
so there actually isn't a single
universally agreed on algorithm for
trigger word detection yet the
literature on trigger word detection
algorithm is still evolving so there
isn't wide consensus yet on what's the
best algorithm for trigger word
detection so I'm just going to show you
one example of an algorithm you can use
now you've seen our ends like this and
what we really do is take an audio clip
maybe compute spectrogram features and
that generates
features x1 x2 x3 audio features x1 x2
x3 that you pass through an RNN and so
all that remains to be done is to define
the target labels Y so if this point in
the audio clip is when someone just
finished saying the trigger word such as
a lecture or saline
hey Suri or okay Google then in the
training sets you can set the target
labels to be zero for everything before
that point and right after that to set
the target label of one and then if a
little bit later on you know the trigger
word was set again and the trigger was
said at this point then you can again
set the target label to be one right
after that now this type of labeling
scheme for an RNN you know could work
actually this won't actually work
reasonably well one slight disadvantage
of this is it creates a very imbalanced
training set so if a lot more zeros than
ones
so one other thing you could do that
it's getting a little bit of a hack but
could make them all the little bit easy
to train is instead of setting only a
single time step to output one you can
actually make an output a few ones for
several times or for a fixed period of
time before reverting back to zero so
and that thumb slightly evens out the
ratio of ones to zeros but this is a
little bit of a hack but if this is when
in the audio clipper trigger where the
set then right after that you can set
the toggle able to one and if this is
the trigger words say the game then
right after that just when you want the
RNN to output one so you get to play
more of this as well in the programming
exercise but so I think you should feel
quite proud of yourself we've learned
enough about the learning that it just
takes one picture at one slide to this
to describe something as complicated as
trigger word detection and based on this
I hope you'd be able to implement
something that works and allows you to
detect trigger words
but you see more of this in the program
exercise so that's it for trigger words
and I hope you feel quite proud of
yourself for how much you've learned
about deep learning that you can now
describe trigger words in just one slide
in a few minutes and that you've been
hopeful II implemented and get it to
work maybe even make it do something fun
in your house that I'm like turn on or
turn off um you could do something like
a computer when you're when someone else
says they trigger words on this is the
last technical video of this course and
to wrap up in this course on sequence
models you learned about rnns including
both gr use and LS TMS and then in the
second week you learned a lot about word
embeddings and how they learn
representations of words and then in
this week you learned about the
attention model as well as how to use it
to process audio data and I hope you
have fun implementing all of these ideas
in this beast program sighs let's go on
to the last video