A back-off discriminative acoustic model for automatic speech recognition
Hung-An Chang, James Glass · 2009
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 sub-problems. By appropriately combining the scores from clas-sifiers 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 per-formance. Experimental results on a large vocabulary lecture transcription task show that the proposed back-off discrimina-tive 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,