Deriving salient learners mispronunciations from cross-language phonological comparisons

This work aims to derive salient mispronunciations made by Chinese (L1 being Cantonese) learners of English (L2 being American English) in order to support the design of pedagogical and remedial instructions. Our approach is grounded on the theory of language transfer and involves systematic phonological comparison between two languages to predict possible phonetic confusions that may lead to mispronunciations. We collect a corpus of speech recordings from some 21 Cantonese learners of English. We develop an automatic speech recognizer by training cross-word triphone models based on the TIMIT corpus. We also develop an "extended" pronunciation lexicon that incorporates the predicted phonetic confusions to generate additional, erroneous pronunciation variants for each word. The extended pronunciation lexicon is used to produce a confusion network in recognition of the English speech recordings of Cantonese learners. We refer to the statistics of the erroneous recognition outputs to derive salient mispronunciations that stipulates the predictions based on phonological comparison.