Clustering algorithm based on the MIBC decision-tree for CSR
基于MBIC的决策树聚类算法在连续语音识别中的应用

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.