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High-order hidden Markov model is known to be more powerful than first-order hidden Markov model and has been applied to a variety of fields. How to better train high-order hidden Markov model is a question of common interest. The EM algorithm together with its derivatives and variations has been the main technique for training hidden Markov model from observational data. We develop a EM algorithm for training any high-order hidden Markov model with multiple observation sequences. In our work, a
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