This information can be used in the HMM framework by applying Bayes rule to compute P (o|c) = P (c|o)P (o) where P (o) and P (c) are the probabilities of observing o and being at c, respectively.
instead of estimating the observation probability P (o|c) directly from training data, we compute it from the classifiers' output.