Trigger Word Detection

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From the course by deeplearning.ai

Sequence Models

6855 ratings

deeplearning.ai

Sequence Models

6855 ratings

Course 5 of 5 in the Specialization Deep Learning

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

From the lesson

Sequence models & Attention mechanism

Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.

Speech recognition8:53

Trigger Word Detection5:02

Meet the Instructors

Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Head Teaching Assistant - Kian Katanforoosh
Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.ai

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

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