I have for some some time tried to understand how this hidden markov model (hmm) works, and have found a lot of tutorials/papers on it which make use of the same examples/principles of explaining the concept.
As far i've understood is the hidden part in hmm due to one not knowing what state you are in. It could be that the observation one is looking at only has a certain probability of occurring in a certain state..
If i perceive the hidden markov model as a function, in which i feed in my observation/oberservations, what is my actual output? A state? a probabilities of the different states?
And depending on the lexicon size, doing this would take quite some time?
And in the case of an ASR/speech recognition system what is a state?... Is it each word?, or is it a phoneme? or something completely different?
how does hmm and gmm work together in different ASR systems?..