Automatic pronunciation error detection based on linguistic knowledge and pronunciation space

This paper presents a new approach that uses linguistic knowledge and pronunciation space for automatic detection of typical phone-level errors made by non-native speakers of mandarin. Firstly, linguistic knowledge of common learner mistakes is embedded in the calculation of log-posterior probability and the revised log-posterior probability (RLPP) is regarded as the measure of mispronunciation; secondly, a restricted pronunciation space is constructed by using RLPP vectors to describe the characteristics of pronunciation and Support Vector Machine (SVM) classifier is applied into the detection of typical pronunciation errors. Experiments based on a nonnative speaker database of mandarin confirm the promising effectiveness of our methods.