Can features be given as input to hidden markov model?

Question

I want to train a HMM classifier with features as input. Considering two observation states(o1, o2) and two hidden states(h1, h2), and some initial probability I apply a supervised algorithm and on the basis of the classifier output, calculate the following Transition prob : [ P(h1/h1), P( h1/ h2); P(h2/ h1),P(h2/h2)]. emission prob: [p(o1/h1), p(o1/h2); p(o2/h1), p(o2/h2)] Is this the correct way to calculate the probabilities?