Imagine that you've got a markov model just like what this article suggests. Now imagine that in each round your markov model emits a state and then sends that state to a noise box which converts that state into some kind of sound.
As a simple model, say S1 leads to a rising C, S2 leads to a falling F, S3 leads to a stable Bb.
In a more complex model, this sound box is actually plays two or three sounds all at once. As a further complexity, many output sounds overlap between states.
If you're a scientist observing just the sounds being made then you're talking about an HMM (because the markov model is "hidden" behind the sound box). We model speech this way by assuming (sort of) that speech is composed of phonemes (each state) which might sort of overlap in their actual sound.
So a scientist using an HMM tries to reverse the process—listen to the sounds and figure out the phonemes that generated it by knowing something about how people make transitions between phonemes in, say, English words.
Comments
Imagine that you've got a markov model just like what this article suggests. Now imagine that in each round your markov model emits a state and then sends that state to a noise box which converts that state into some kind of sound.
As a simple model, say S1 leads to a rising C, S2 leads to a falling F, S3 leads to a stable Bb.
In a more complex model, this sound box is actually plays two or three sounds all at once. As a further complexity, many output sounds overlap between states.
If you're a scientist observing just the sounds being made then you're talking about an HMM (because the markov model is "hidden" behind the sound box). We model speech this way by assuming (sort of) that speech is composed of phonemes (each state) which might sort of overlap in their actual sound.
So a scientist using an HMM tries to reverse the process—listen to the sounds and figure out the phonemes that generated it by knowing something about how people make transitions between phonemes in, say, English words.