Pronunciation Quality Evaluation Based on Phone Scoring Model
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Posterior probability is a promising feature for computers to judge testers' pronunciation quality in computer assisted language learning systems.However,the discrepancy between posterior probability and evaluators' criteria is obvious.This paper introduces "Phone Scoring Model" which transforms posterior probability to deal with the problem.Both linear and non-linear phone scoring models are investigated and we find that: close formed solution can be obtained for linear phone scoring models and gradient descent method can be used for nonlinear phone scoring models.Experimental results based on 498 people's live PSC database indicate that this approach can significantly improve system performance: approximately 42% relative performance gain when posterior probabilities are calculated with all-phone probability space;approximately 23%~27% relative performance gain when probabilities are calculated with optimized probability spaces.