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This video just has a plain transcript, not time-aligned to the videoTHIS IS AN UNCORRECTED AUTOMATIC TRANSCRIPT. IT MAY BE CORRECTED LATER IF TIME PERMITS
so we're almost there.
The hidden Markov model speech is a signal that changes to time.
Let's just remind ourselves what some speech looks like.
So there's some speech changes through time.
Maybe for this little 25 milliseconds.
That's not, Let's pretend 25 milliseconds.
The spectrum is approximately certain shape on for the next frame.
It's really similar, so those could be generated from the same probability distribution.
But at some point maybe about here.
Things change quite a lot, so we might want to have all of these frames generated one particular calcium.
Maybe that's these guys here.
And then, at some point that this is the word may be moving into the next sound in the word the sounds here they're all statistically similar.
They all look like the same distribution.
So where they could be generated from a separate galaxy And maybe that's these guys here and then at the end.
Maybe all of this stuff has a similar spectral envelope.
It's statistically similar, and that could be generated by this guy's here.
Of course, we got some design decisions to make.
About the particularly unit were modelling is a phone in a word on How many galaxy ins in a row do we need to generate that? That's something you're going to explore in the lab.
You can vary that.
You could see what the effect of that is on the accuracy of the model.
So we got to choose got gallstones.
12 and three.
We could have four off 10.
Any number.
We want this thing that we modelling.
It could be a word which is always going to be in the lab.
It could be the phone.
It could be anything we want, right? So all we need to do now is that given we're going to generate from the sequence of KAOS wins in a row, we just need to make sure that things happen in the right order.
So our model has got three garrisons in it.
This one, this one and this one on DH.
He's going to be learned from data.
We're going to do the learning of this model at the end of the course because it's the hardest part.
For now, we're just going to pretend we know how to get these gals Ian's magic.
You're going to give them to you, given these gals Ian's.
Our model is just a box labelled school model.
A happens to be of the word.
Yes, and in the box there's some parameters the promises of the model on the parameters of these three guardians This one this one on this one remember, all the guardian is is just a mean understand deviation or variance.
So we just write numbers down for those things.
Know we need to do now is when we use this model to generate on seen examples or to assign a probability to an unseen example, we better make sure to use this gas is in the right order.
So we better generate from this one first, then this one and then this one.
It makes no sense to reorder these things that we like doing dynamic time warping where we compare the beginning with the beginning.
Then the middle with the end and then the end with the middle Never make sense for speech.
Speech is what this sequence is ordering.
So we just need to have a little mechanism to make sure that when we generate from the model in the three garrisons in the box, we start with first one, then the second one, then the third one.
So we need We're just going to do that with the simplest possible way.
Little finite state machine.
The finite state machines states a finite number of those states.
In this example, the three has transitions that tell us the order were allowed to go in between the finite state machines.
We could do things like this.
We could go to this state.
We could stay there for a bit, and then we could move on.
Stay there for a bit.
Move on.
Staying this one from it.
Move on.
What we can't do is go backwards.
That would be like doing time.
Reverse with the speak signal that never make sense except House of Silence.
So we gotta papa galleons into the finite state machines just to get this order and constraint.