A Synchronous Method for Automatic Scoring of Language Learning

Abstract

In this paper, a synchronous method based on state graph is proposed to calculate the evaluation feature for automatic scoring in computer-assisted language learning (CALL). The posterior probabilities of states are selected as the main feature. The score of hypothesized phonemes and words are estimated using the information of corresponding states. Traditional systems use two passes and two different models for decoding and computing posterior probabilities respectively. In this new algorithm, the posterior probabilities are calculated during the decoding of the state graph constructed from grammar. And in this new algorithm, the same acoustics model is used during the process of decoding and posterior probabilities computing. The old and new computing algorithms are compared through experiments, and the result shows that performance of the new algorithm is effectively improved. The scoring accuracy of new synchronous algorithm is improved, while the computing complexity reduces 16%.