Abstract

We propose a new method for detecting out-of-vocabulary (OOV) words for large vocabulary continuous speech recognition (LVCSR) systems. Our method is based on performing a joint alignment between independently generated word and phone lattices, where the word-lattice is aligned via a recognition lexicon. Based on a similarity measure between phones, we can locate highly mis-aligned regions of time, and then specify those regions as candidate OOVs. This novel approach is implemented using the framework of graphical models (GMs), which enable fast flexible integration of different scores from word lattices, phone lattices, and the similarity measures. We evaluate our method on switchboard data using RT-04 as test set. Experimental results show that our approach provides a promising and scalable new way to detect OOV for LVCSR. Index Terms — out-of-vocabulary, OOV, lattices, graphical models, Bayesian networks, dynamic Bayesian networks

Citations