Abstract
Maximum Likelihood Estimation (MLE) is a traditional method for training acoustic models for speech recognition. This method does not consider discriminative relation between acoustic models, so some models are apt to obscure each other. In order to raise the differentiation degree between models, discriminative training criteria are proposed. Seeing that Minimum Phone Error (MPE) criterion has great progress reported in the literature, we apply MPE to continuous phone speech recognition system in this thesis. The procedure is to adopt MLE to train acoustic models first, and then use MPE to refine the models again. According to the experimental result, MPE can reduce phone error rate further. In general, MPE adopts phone lattice to express all possible sentences. In order to improve the efficiency, we use N-Best list to construct a phone lattice which is called N-Best Synthesized Lattice. Besides, in order to distinguish obscure phones and remove repeated words that appear in very close time, we use another kind of phone lattice called sausage that can improve the results of MPE.