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A stronger canonical model was developed to improve the performance of automatic pronunciation evaluations.Three different strategies were investigated with speaker adaptive training to normalize variations among speakers,minimum phone error training to identify easily confused phones and maximum likelihood linear regression(MLLR) adaptation to compensate for accent variations between native and non-native speakers.The three schemes were combined to improve the correlation coefficient between machine scores and human scores from 0.651 to 0.679 on the sentence level and from 0.788 to 0.822 on the speaker level.
A stronger canonical model was developed to improve the performance of automatic pronunciation evaluations.Three different strategies were investigated with speaker adaptive training to normalize variations among speakers, minimum phone error training to identify easily confused phones and maximum likelihood linear regression (MLLR) adaptation to compensate for accent variations between native and non-native speakers. The three schemes were combined to improve the correlation coefficient between machine scores and human scores from 0.651 to 0.679 on the sentence level and from 0.788 to 0.822 on the speaker level.