Abstract
In discriminative training, such as Maximum Mutual Information Estimation (MMIE) training, a word lattice is usually used as a compact representation of many different sentence hypotheses and hence provides an efficient representation of the confusion data. However, in a large vocabulary continuous speech recognition (LVCSR) system trained from hundreds or thousands hours training data, the extended Baum-Welch (EBW) computation on the word lattice is still very expensive. In this paper, we investigated the effect of lattice pruning on MMIE training, where we tested the MMIE performance trained with different lattice complexity. A beam pruning and a posterior probability pruning method were applied to generate different sizes of word lattices. The experimental results show that using the posterior probability lattice pruning algorithm, we can save about 40% of the total computation and get the same or more improvement compared to the baseline MMIE result.
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