The use of accent-specific pronunciation dictionaries in acoustic model training

Abstract

Speech recognition systems are increasingly being built to cover an ever wider range of speaker accents. However, electronically available pronunciation dictionaries (PDs) specific to these accents often do not exist and would be time consuming and expensive to build by hand. This paper explores the use of pronunciation modelling for the synthesis of accent-specific PDs directly from acoustic data, and their use in acoustic model training. It is shown that this is particularly effective when the amount of acoustic data from the new accent region is insufficient to build a new recogniser, and it is necessary to retrain an existing system: a further 15% reduction in word error rate can be achieved over and above the 20% reduction resulting from acoustic model retraining alone. This paper also presents an empirical evaluation of an American English PD which has been synthesised from a British English PD.