Hmm State Clustering Based on Efficient Cross-Validation
Hmm State Clustering Based on Efficient Cross-Validation
复制标题
基于高效交叉验证的Hmm状态聚类
DOI：
10.1109/icassp.2006.1660231
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
T. Shinozaki
中科院分区：
文献类型：
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作者：
T. Shinozaki
决策树状态聚类探讨使用交叉验证似然准则。交叉验证似然比常规似然更可靠，并且可以使用足够的统计数据来有效地计算。它导致一个更好的搭售结构，并提供了一个终止标准，不依赖于经验的阈值。对大词汇量电话语音的识别实验表明，对于大量的绑定状态，交叉验证方法给出了更鲁棒的结果
Decision tree state clustering is explored using a cross validation likelihood criterion. Cross-validation likelihood is more reliable than conventional likelihood and can be efficiently computed using sufficient statistics. It results in a better tying structure and provides a termination criterion that does not rely on empirical thresholds. Large vocabulary recognition experiments on conversational telephone speech show that, for large numbers of tied states, the cross-validation method gives more robust results