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Evaluating spoken language model based on filler prediction model in speech recognition
We propose a method that uses a filler prediction model for building a language model that includes fillers from a corpus without fillers. In our method, a filler prediction model is trained from a corpus that does not cover domain-relevant topics. It recovers fillers in inexact transcribed corpora in the target domain, and then a language model that includes fillers is built from the corpora. The results of an evaluation of the Japanese National Diet Record showed that a model using our methoddoi:10.21437/interspeech.2008-256 fatcat:acecpixvi5axnbw7edf7gjv3uy