#3 i-vectors youtube auto transcript

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hello this is daniel povey and today
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we're asking him what's the difference
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between eye vectors and x vectors
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okay so
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i vectors and x vectors are both
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concepts from uh speaker recognition
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meaning like speaker identification so
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it's basically a fixed dimensional
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vector of let's say the dimension 256 or
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512 or something like that
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the the
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it's supposed to represent the
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information about the speaker
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uh
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but the uh
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the special thing the original thing
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about eye vectors was
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the you extract an eye vector from like
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just a recording
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and it
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it contains information about both the
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speaker and the uh
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the kind of recording conditions
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and then you use other methods to
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separate the to separate those two
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sources of variation like plda and stuff
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but for cali purposes
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we mostly use eye vectors
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for uh
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a very basic form of speaker adaptation
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so that when we train a neural network
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we input the eye vector as a kind of
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extra input to the neural network and it
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helps it to adapt and actually
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for the most part it just acts
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it has a similar effect to just like
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mean normalization or something like
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that because it can use the eye vector
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to figure out
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you know what's roughly the mean of the
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uh the input feature so actually in the
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end i kind of regretted in
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regretting putting the eye vector stuff
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in because you can get most of the
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improvement just from uh
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giving it the mean of the features up
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till the present point
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so anyway so that's what i vectors are
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now x vectors is a kind of
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a neural net version of i vectors where
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uh
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you basically train a neural net to
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discriminate between speakers
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and inside the neural net it has
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there's some kind of embedding layer
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that's just before the classifier
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and and you call that the x vector so
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you can extract basically it's a way of
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extracting a fixed dimensional feature
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from an occurrence now the thing with
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both eye vectors and x vectors is that
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to train the
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the classifier effectively to train the
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system that extracts the eye vector or
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or the x factor you need a very huge
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amount of data so for eye vectors
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ideally
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you want like a thousand hours or
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something
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if it's for speaker identification
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purposes and for x vectors like ideally
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you want something like 10 000 hours
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which is a bit ridiculous now for speech
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recognition
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it's not as critical so it's fine if you
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have just like 10 hours or 100 hours
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because we're not really using it for
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speaker identification we're just using
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it for a basic
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form of adaptation so
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it's not so critical
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okay so does quality use x vectors at
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all
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uh well
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there are uh
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speaker recognition recipes in caldi
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like if you look at sre 16
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things like that
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uh though those that's not for speech
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recognition though because
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uh there's no advantage of x vectors
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over i vectors for uh
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for its application to speech
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recognition we're just using it like i
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said for basic adaptation
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and we don't really need all of that uh
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discriminating power of x vectors so
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answer is we're using it only for speed
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for speaker recognition
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okay thank you
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thank you