Round-robin duel discriminative language models in one-pass decoding with on-the-fly error correction

This paper focuses on discriminative n-gram language models for large vocabulary speech recognition. We have proposed a novel training method called the round-robin duel discrimination (R2D2) method. Our previous report showed that R2D2 outperforms conventional methods on word n-gram based discriminative language models (DLMs). In this paper, we achieve additional error reduction and one-pass decoding at the same time. The keys to achieving this are the use of morphological features and the on-the-fly composition of weighted finite-state transducers (WFSTs) that represent both word and morphological discriminative features. Our experimental results show that R2D2 can reduce recognition errors more effectively than conventional methods in the reranking of n-best hypotheses and one-pass decoding can be accomplished with an equivalent accuracy.