We investigate the use of joint-sequence multigrams to generate L2 mispronunciation lexicons for mispronunciation detection and diagnosis. In the joint-sequence framework, a pair of parallel strings (namely, the input string of either graphemes or phonemes of the canonical pronunciation and the phonetic string of the mispronunciation) are aligned to form joint units for probabilistic estimation. We compare results on lexicons produced by phoneme-to-mispronunciation conversion and those by graphemeto- mispronunciation conversion. Results reflect the hypothesized advantage (1.1% reduction in expected miss rate) in unifying phonetic confusion due to L1 negative transfer with those due to grapheme-to-phoneme errors. The impact of mispronunciation by mis-use of analogy is also studied. Recognition results show the benefit of a lexicon with proper priors.
Bibliographic reference. Qian, Xiaojun / Meng, Helen / Soong, Frank K. (2011): "On mispronunciation lexicon generation using joint-sequence multigrams in computer-aided pronunciation training (CAPT)", In INTERSPEECH-2011, 865-868.