基于MBIC的决策树聚类算法在连续语音识别中的应用

提出了一种采用最小贝叶斯信息准则（Minimum Bayesian Information Criterion,MBIC）来最优化控制决策树结点分裂程度的算法。首先在理论上证明了MBIC能够较好地解决模型参数复杂度与训练数据集规模之间的权衡问题，然后给出了基于MBIC的决策树分裂停止准则的计算公式。汉语连续语音全音节识别实验表明：与传统的最大似然准则（Maximum Likeihood Criterion,MLC）相比，MBIC对声学模型参数和训练数据集的变化具有更好的适应能力。

Clustering algorithm based on the MIBC decision-tree for CSR

an algorithm based on Minimum Bayesian Information Criterion(MBIC) was proposed to help optimize the node-splitting degree in a decision tree.First,it was proved in theory that MBIC can find a good balance between the complexity of model parameters and the scale of the training sets.Then,a formula was proposed to describe MBIC decision tree splitting and stopping criterion.Finally,the experiment on Chinese all-syllable recognition shows that MBIC has much better adaptive ability to variable acoustic model parameters and training sets than the classical Maximum Likeihood Criterion method.