Minimum Bayes risk discriminative language models for Arabic speech recognition

Abstract

In this paper we explore discriminative language modeling (DLM) on highly optimized state-of-the-art large vocabulary Arabic broadcast speech recognition systems used for the Phase 5 DARPA GALE Evaluation. In particular, we study in detail a minimum Bayes risk (MBR) criterion for DLM. MBR training outperforms perceptron training. Interestingly, we found that our DLMs generalized to mismatched conditions, such as using a different acoustic model during testing. We also examine the interesting problem of unsupervised DLM training using a Bayes risk metric as a surrogate for word error rate (WER). In some experiments, we were able to obtain about half of the gain of the supervised DLM.