Minimum error rate training based on N-best string models

Abstract

The authors study issues related to string level acoustic modeling in continuous speech recognition. They derive the formulation of minimum string error rate training. A minimum string error rate training algorithm, segmental minimum string error rate training, is described. It takes a further step in modeling the basic speech recognition units by directly applying discriminative analysis to string level acoustic model matching. One of the advantages of this training algorithm lies in its ability to model strings which are competitive with the correct string but are unseen in the training material. The robustness and acoustic resolution of the unit model set can therefore be significantly improved. Various experimental results have shown that significant error rate reduction can be achieved using this approach. >