Pronunciation Variants Prediction Method to Detect Mispronunciations by Korean Learners of English

Abstract

This article presents an approach to nonnative pronunciation variants modeling and prediction. The pronunciation variants prediction method was developed by generalized transformation-based error-driven learning (GTBL). The modified goodness of pronunciation (GOP) score was applied to effective mispronunciation detection using logistic regression machine learning under the pronunciation variants prediction. English-read speech data uttered by Korean-speaking learners of English were collected, then pronunciation variation knowledge was extracted from the differences between the canonical phonemes and the actual phonemes of the speech data. With this knowledge, an error-driven learning approach was designed that automatically learns phoneme variation rules from phoneme-level transcriptions. The learned rules generate an extended recognition network to detect mispronunciations. Three different mispronunciation detection methods were tested including our logistic regression machine learning method with modified GOP scores and mispronunciation preference features; all three methods yielded significant improvement in predictions of pronunciation variants, and our logistic regression method showed the best performance.