Personalized mispronunciation detection and diagnosis based on unsupervised error pattern discovery

Abstract

In this work, we introduce two improvements to our previously proposed mispronunciation detection framework. The framework focuses on each learner individually and consists of two main procedures: unsupervised error pattern discovery and pronunciation error decoding. First, we propose nbest filtering to disambiguate uncertain error candidate hypotheses obtained from acoustic similarity clustering. Second, we propose personalized template-based rescoring to refine the mispronunciation detection results. The second contribution of the paper is that we demonstrate the portability of the framework to a new target language. Experimental results on the iCALL corpus, a nonnative Mandarin corpus consisting of speakers of European origin, show that the new error pattern discovery process significantly reduces the size and increases the coverage of the error candidate set. Also, the rescoring technique effectively improves system performance on mispronunciation detection and diagnosis.