An empirical study of automatic accent classification

Abstract

This paper extends language identification (LID) techniques to a large scale accent classification task: 23-way classification of foreign-accented English. We find that a purely acoustic approach based on a combination of heteroscedastic linear discriminant analysis (HLDA) and maximum mutual information (MMI) training is very effective. In contrast to LID tasks, methods based on parallel language models prove much less effective. We focus on the Oregon Graduate Institute Foreign-Accented English dataset, and obtain a detection rate of 32%, which to our knowledge is the best reported result for 23-way accent classification.