Minimum word error based discriminative training of language models

This paper considers discriminative training of language models for large vocabulary continuous speech recognition. The minimum word error (MWE) criterion was explored to make use of the word confusion information as well as the local lexical constraints inherent in the acoustic training corpus, in conjunction with those constraints obtained from the background text corpus, for properly guiding the speech recognizer to separate the correct hypothesis from the competing ones. The underlying characteristics of the MWEbased approach were extensively investigated, and its performance was verified by comparison with the conventional maximum likelihood (ML) approaches as well. The speech recognition experiments were performed on the broadcast news collected in Taiwan.