Pronunciation Proficiency Evaluation based on Discriminatively Refined Acoustic Models

The popular MLE (Maximum Likelihood Estimation) is a generative approach for acoustic modeling and ignores the information of other phones during training stage. Therefore, the MLE-trained acoustic models are confusable and unable to distinguish confusing phones well. This paper introduces discriminative measures of minimum phone/word error (MPE/MWE) to refine acoustic models to deal with the problem. Experiments on the database of 498 people’s live Putonghua test indicate that: 1) Refined acoustic models are more distinguishable than conventional MLE ones; 2) Even though training and test are mismatch, they still perform significantly better than MLE ones in pronunciation proficiency evaluation. The final performance has approximately 4.5% relative improvement.