The most popular estimation method for HMMs is Baum Welch algorithm, which is based on the maximum likelihood(ML) criterion. For other criteria, such as Maximum Mutual Information (MMI) criterion, such an algorithm does not exist. In this case, a gradient based method is considered. With the complexity of the objective function, the computation of the gradients has to be solved before it can be applied to this problem. This paper proposed an implementation method of the gradient based method. Experimental results indicate that this method produces monotonous improvement like Baum Welch algorithm.
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机译:最小特征值的上限和下限,分别是包含alpha参数的问题中的能量整数。 Obere und Untere schranken fuer den Tiefsten Eigenwert Bzw.Das Energieintegral in problemen,die Einen parameter alpha Enthalten