Training – uniform segmentation

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 first going to do a really quick and dirty solution that does operate in one step.
That gives you a rather poor model.
Then we're going to do on it primitive method that gets you a lot better model but not perfect, because only constitutes a single state sequence.
And then we look at the real thing and we'll look at it mainly qualitatively just understand its properties.
And if you want the mask there in the books, I'm not expecting you to understand the mathematical formulation of this full album.
Do you want to? You don't understand conceptually why it's better than doing this.
You, Toby approximation.
I'm going to ignore transition probabilities just going only to say that as we're doing all of these alignments, essentially, we could just count how many times each transition was used normalised those counting to probabilities.
So you're actually quite easy to estimate transition probabilities, but we're just going to ignore them and concentrate on the most important parameter, which is the meaning variance of calcium.
So here's a really quick and dirty way of doing it is not gonna give us a very good model.
So in all these examples.
We've got a model and it's got three emitting states.
So five state model in H K speak, we got an observation sequence that has six observations.
Each of those is a vector of MFC sees, perhaps with 39 dimensions.
Andi, we can see that there's more than one way this model could have generated observation sequence.
One of those paths is this very crude one.
It just says we spend about the same amount of time in each of the three states.
If six divided into three nicely, that's good, because I chose carefully.
Example.
Didn't divide nicely.
We'll just have to crudely divide it.
We're just assign uniformly will slice this into three parts.
We're just crudely assigned the 1st 3rd to the first state and so on.
Okay, so we'll take these two observations thes actual FCC vectors that came from a little 25 millisecond bit of speech.
They have values.
We'll take these, too.
I will add them together and divide by two, and that will become the mean of the galaxy in this state.
That's an empirical estimate of mean, and we'll take their average squared distance in the mean time we'll assign that as the variables.
And if we're using full co variance, we have a matrix.
But in all these examples, we just have a vector and we do the same here.
These two, we assume that were generated by this galaxy and there'll be this one and its variants.
And so that's clearly over simplistic because we know our speaks changes in duration, for example, speaking rate.
It doesn't stretch linearly.
So we wouldn't want to just crudely say the first model always models the 1st 3rd of this phoney more word.
It might model the first sound in this phoney more word which might not stretch linearly like that.
However, this is going to get a straight to some parameters of the model.
The mean of this state, the mean of this state, the mean of this state will be different.
This one will be from here.
This next one will be from here, this one from here.
So they'll be different Libby crude approximations to the true model.
So we got somewhere from a model with no parameters.
We've got a crude model with some parameters.
It was no need to repeat this because every time we do it, we'll get the same.
Yeah, we're happy.
That idea.
This is just deterministic if I do it a second time different.
So it's a one step algorithm was not very good.
Let's just call uniforms.
Segmentation, for obvious reasons, I guess, is a crude model.
That's a good start.
Take the statistics off the observations alone with state, and those are the estimates of the parameters of that state.
So if solve the problem off which observations were generated by which state by making this rather simple assumption that they're just uniformly segmented.