Detection of out-of-vocabulary words in posterior based ASR

Over the years, sophisticated techniques for utilizing the prior knowledge in the form of text-derived language model and in pronunciation lexicon evolved. However, their use has an undesirable effect: unexpected lexical items (words) in the phrase are replaced by acoustically acceptable in-vocabulary items [1]. This is the major source of error since the replacement often introduces additional errors [2, 3]. Improving the machine ability to handle these unexpected words would considerably increase the utility of speech recognition technology.