In this paper we propose discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition tasks. After presenting our hierarchical modeling framework, we describe how the models can be generated with either Minimum Classification Error or large-margin training. Experiments on a large vocabulary lecture transcription task show that the hierarchical model can yield more than 1.0 % absolute word error rate reduction over non-hierarchical models for both kinds of discriminative training. Index Terms — hierarchical acoustic modeling, discriminative training, LVCSR 1.

In this paper we propose a back-off discriminative acoustic model for Automatic Speech Recognition (ASR). We use a set of broad phonetic classes to divide the classification problem originating from context-dependent modeling into a set of subproblems. By appropriately combining the scores from classifiers designed for the sub-problems, we can guarantee that the back-off acoustic score for different context-dependent units will be different. The back-off model can be combined with discriminative training algorithms to further improve the performance. Experimental results on a large vocabulary lecture transcription task show that the proposed back-off discriminative acoustic model has more than a 2.0 % absolute word error rate reduction compared to clustering-based acoustic model. Index Terms: context-dependent acoustic modeling, back-off acoustic models, discriminative training,