Using accent-specific pronunciation modelling for improved large vocabulary continuous speech recognition

A method of modelling accent-specific pronunciation variations is presented. Speech from an unseen accent group is phonetically transcribed such that pronunciation variations may be derived. These context-dependent variations are clustered in decision trees which are used as a model of the pronunciation variation associated with this new accent group. The trees are then used to build a new pronunciation dictionary for use during the recognition process. Experiments are presented, based on Wall Street Journal and WSJCAM0 corpora, for the recognition of American speakers using a British English recogniser. Speaker independent as well as speaker dependent adaptation scenarios are presented, giving up to 20% reduction in word error rate. A linguistic analysis of the pronunciation model is presented and finally the technique is combined with maximum likelihood linear regression, a well proven acoustic adaptation technique, yielding further improvement.