Pronunciation Proficiency Evaluation based on Discriminatively Refined Acoustic Models
Автор: Ke Yan, Shu Gong
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 2 Vol. 3, 2011 года.
Бесплатный доступ
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.
Computer assisted language learning, MPE, MWE, posterior probability, PSC, discriminative training
Короткий адрес: https://sciup.org/15011612
IDR: 15011612
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