The segmental K-means algorithm for estimating parameters of hidden Markov models

The authors discuss and document a parameter estimation algorithm for data sequence modeling involving hidden Markov models. The algorithm, called the segmental K-means method, uses the state-optimized joint likelihood for the observation data and the underlying Markovian state sequence as the objective function for estimation. The authors prove the convergence of the algorithm and compare it with the traditional Baum-Welch reestimation method. They also print out the increased flexibility this algorithm offers in the general speech modeling framework. >

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