Calibration of machine scores for pronunciation grading
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چکیده
Our proposed paradigm for automatic assessment of pronunciation quality uses hidden Markov models (HMMs) to generate phonetic segmentations of the student’s speech. From these segmentations, we use the HMMs to obtain spectral match and duration scores. In this work we focus on the problem of calibrating different machine scores to obtain an accurate prediction of the grades that a human expert would assign to the pronunciation. We discuss the application of different approaches based on minimum mean square error (MMSE) estimation and Bayesian classification. We investigate the characteristics of the different mappings as well as the effects of the prior distribution of grades in the calibration database. We finally suggest a simple method to extrapolate mappings from one language to another.
منابع مشابه
Combination of machine scores for automatic grading of pronunciation quality
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تاریخ انتشار 1998