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This paper proposes a new estimation algorithm for the parameters of an HMMas to best account for the observed data. In this model, in addition to theobservation sequence, we have \emph{partial} and \emph{noisy} access to thehidden state sequence as side information. This access can be seen as "partiallabeling" of the hidden states. Furthermore, we model possible mislabeling inthe side information in a joint framework and derive the corresponding EMupdates accordingly. In our simulations, we observe that using this sideinformation, we considerably improve the state recognition performance, up to70%, with respect to the "achievable margin" defined by the baselinealgorithms. Moreover, our algorithm is shown to be robust to the trainingconditions.

A Novel Training Algorithm for HMMs with Partial and Noisy Access to the States