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We present two families of matrix-variate hidden Markov regression models, which differ in how they handle covariates (i.e., as fixed or random). The models achieve parsimony by using the eigen-decomposition of the components’ covariance matrices. A two-step fitting strategy is implemented due to the high number of parsimonious models. These models are then investigated on a real dataset.