Cross-Validation EM Training for Robust Parameter Estimation
Cross-Validation EM Training for Robust Parameter Estimation
复制标题
DOI：
10.1109/icassp.2007.366943
复制
发表时间：
2007-06
期刊：
影响因子：
--
通讯作者：
T. Shinozaki;Mari Ostendorf
中科院分区：
文献类型：
--
作者：
T. Shinozaki;Mari Ostendorf
A new maximum likelihood training algorithm is proposed that compensates for weaknesses of the EM algorithm by using cross-validation likelihood in the expectation step to avoid overtraining. By using a set of sufficient statistics associated with a partitioning of the training data, as in parallel EM, the algorithm has the same order of computational requirements as the original EM algorithm. Analyses using a GMM with artificial data show the proposed algorithm is more robust for overtraining than the conventional EM algorithm. Large vocabulary recognition experiments on Mandarin broadcast news data show that the method makes better use of more parameters and gives lower recognition error rates than EM training.
