Abstract
Computer-aided pronunciation training (CAPT) technologies enable the use of automatic speech recognition to detect mispronunciations in second language (L2) learners’ speech. In order to further facilitate learning, we aim to develop a principle-based method for generating a gradation of the severity of mispronunciations. This paper presents an approach towards gradation that is motivated by auditory perception. We have developed a computational method for generating a perceptual distance (PD) between two spoken phonemes. This is used to compute the auditory confusion of native language (L1). PD is found to correlate well with the mispronunciations detected in CAPT system for Chinese learners of English, i.e., L1 being Chinese (Mandarin and Cantonese) and L2 being US English. The results show that auditory confusion is indicative of pronunciation confusions in L2 learning. PD can also be used to help us grade the severity of errors (i.e., mispronunciations that confuse more distant phonemes are more severe) and accordingly prioritize the order of corrective feedback generated for the learners.
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This work is supported by the National Basic Research 973 Program of China under Grant No. 2013CB329304, the National Natural Science Foundation of China under Grant No. 61370023, and the Major Project of the National Social Science Foundation of China under Grant No. 13&ZD189. This work is also partially supported by the General Research Fund of the Hong Kong SAR Government under Project No. 415511 and the CUHK Teaching Development Grant.
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Jia, J., Leung, WK., Wu, YH. et al. Grading the Severity of Mispronunciations in CAPT Based on Statistical Analysis and Computational Speech Perception. J. Comput. Sci. Technol. 29, 751–761 (2014). https://doi.org/10.1007/s11390-014-1465-2
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DOI: https://doi.org/10.1007/s11390-014-1465-2