While OOV is always a problem for most languages in ASR, in the Chinese case the problem can be avoided by utilizing character n-grams and moderate performances can be obtained. However, character ngram has its own limitation and proper addition of new words can increase the ASR performance. Here we propose a discriminative lexicon adaptation approach for improved character accuracy, which not only adds new words but also deletes some words from the current lexicon.