Towards single pass discriminative training for speech recognition

This paper describes how we can combine our previously proposed fast extended Baum-Welch algorithm and generalized discriminative feature transformation to achieve single pass discriminative training, which we only process the data once. Compared to the state of the art training procedure, which uses feature space maximum mutual information (fMMI) and boosted maximum mutual information (BMMI), our proposed training procedure can achieve around 80% of the improvement available from discriminative training. We also show that if we are allowed to process the data twice, it is possible to achieve almost all of the improvement. We evaluate different training procedures on various large scale tasks using Iraqi and modern standard Arabic speech recognition systems.