A Comparison of Two Unsupervised Approaches to Accent Identification

Abstract

The ability to automatically identify a speaker's accent would be very useful for a speech recognition system as it would enable the system to use both a pronunciation dictionary and speech models specific to the accent, techniques which have been shown to improve accuracy. Here, we describe some experiments in unsupervised accent classification. Two techniques have been investigated to classify British- and American-accented speech: an acoustic approach, in which we analyse the pattern of usage of the distributions in the recogniser by a speaker to decide on his most probable accent, and a high-level approach in which we use a phonotactic model for classification of the accent. Results show that both techniques give excellent performance on this task which is maintained when testing is done on data from an independent dataset.