Developing a hybrid language model for open vocabulary automatic speech recognition in a lecture speech task

Abstract

This paper addresses the problem of open vocabulary automatic speech recognition (ASR) using hybrid statistical language models (LMs). Hybrid LMs differ from closed vocabulary LMs in that the word level lexicon is augmented with an inventory of sub-lexical units (SLUs). The procedures used for selecting these SLUs and expanding out-of-vocabulary (OOV) words according to the SLUs is presented in the paper. The open-vocabulary ASR performance obtained using these techniques is presented for a lecture speech task domain.