Pronunciation Space Models for Pronunciation Evaluation

Posterior probability is mostly used for pronunciation evaluation. This paper introduces pronunciation space models to calculate posterior probability replacing traditional phone-based acoustic models, which makes the calculated posterior probability more precise. Pronunciation space models are constructed using unsupervised clustering method guided by human scores and phone-level posterior probability. By using correlation between machine scores and human scores as the performance measurement, pronunciation space models based method shows its effectiveness for pronunciation evaluation in the experiments on a Chinese database spoken by Koreans with the correlation´s improvement from 0.390 to 0.415 comparing to the traditional method based on phone based acoustic models.