Experimental Investigation of Mandarin Pronunciation Quality Assessment System

As the most effective confidence measure in computer assisted language learning system, the posterior probability is used widely, in which some tricks are applied to reduce the computation complexity. In this paper, we analysis the defect of the traditional algorithm and propose some improvements. Firstly, the traditional algorithm adopts the method of maximum instead of sum in the calculation of the denominator, which seriously reduces the accuracy of posterior probability. Therefore, taking into account both computation complexity and system performance, we propose a novel algorithm based on phoneme confusion extended network. Secondly, in the traditional algorithm, the posterior probability is normalized by its segment time. Infact, the acoustic likelihood is more related with time and grows with the frame number. So we propose the acoustic likelihood based normalization algorithm. Experiment results show that compared to traditional algorithm, the proposed algorithm can improve system performance significantly, about 35% average score error rate relatively, and the computation complexity is hardly increased.