Phonological modeling of mispronunciation gradations in L2 English speech of L1 Chinese learners

Generation of corrective feedback carries significant pedagogical importance in the design of computer-aided pronunciation training systems. Such feedback generation should take into account the severity of detected mispronunciations, in order to prioritize different kinds of corrections to be conveyed to the learner. However, mispronunciation gradation is highly dependent on the phonetic context and acoustic context of the word pronunciation, as well as human perception. We have defined several categories of mispronunciation gradation, ranging from subtle to salient, and collected crowdsourced ratings from a large number of listeners. This work aims to capture the phonetic context of word mispronunciation by phonological rules, which are then augmented with statistical scoring to quantitatively model mispronunciation gradations. The model can thus be used to generate gradation ratings of word mispronunciations, especially those that are previously unseen in the training set. We will report the results of automatic gradation classification, as well as its correlation(s) with human perception.