An Mandarin Pronunciation Quality Assessment System Using Two Kinds of Acoustic Models

This paper presents our Mandarin pronunciation quality assessment system for the examination of Putonghua Shuiping Kaoshi (PSK) and investigates some measures to improve the assessment accuracy. In this paper, a selective speaker adaptation method is studied. In the adaptation module, we select well pronounced speech as the adaptation data, and adopt Maximum Likelihood Linear Regression (MLLR) to update the speaker-independent (SI) acoustic model. Besides the triphone based acoustic model, the monophone based acoustic model is also applied to our system. Further improvements are obtained by combining posterior probabilities computed with triphone and monophone based acoustic models using Support Vector Machine (SVM) to assess the goodness of pronunciations. The experiment results show that the average correlation coefficient (ACC) between machine and the human scores achieves 0.8549, almost equivalent to ACC between different experts. The improved system achieves usable performance in actual applications.