Automatic intelligibility assessment and diagnosis of critical pronunciation errors for computer-assisted pronunciation learning

Abstract

We introduce a novel method to diagnose pronunciation errors that are most critical to the intelligibility of L2 learners. A preliminary study showed that error rates computed by a speech recognition-based system can be used to characterize intelligibility. We deduce a probabilistic algorithm to derive intelligibility from error rates. We also define an error priority function that indicates which errors are most critical to intelligibility. Experimental results proved the validity of the approach.