An Empirical Study of Automatic Accent Classification

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 languagemodels provemuch

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.

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In  In Proceedings of ICASSP

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