Automatic mispronunciation detection for Mandarin

This paper presents the methods to improve the performance of mispronunciation detection at syllable level for Mandarin from two aspects: proposing scaled log-posterior probability (SLPP) and weighted phone SLPP to get the better measure of pronunciation quality; introducing speaker normalization of speaker adaptive training (SAT) and speaker adaptation of selective maximum likelihood linear regression (SMLLR) to get a better statistical model. Experiments based on a database, consisting of 8000 syllables pronounced by 40 speakers with varied pronunciation proficiency, confirm the promising effectiveness of these strategies by reducing FAR from 41.1% to 31.4% at 90% FRR and 36.0% to 16.3%at 95%FRR.