Toward unsupervised discovery of pronunciation error patterns using universal phoneme posteriorgram for computer-assisted language learning

In Computer-Aided Pronunciation Training, we hope to specify the type of mispronunciation, or Error Pattern (EP), the language learner has made as a more effective feedback. But derivation of EPs usually requires expert knowledge and pedagogical experiences, which is not easy to obtain for each pair of target and native languages. In this paper we propose a preliminary framework toward unsupervised discovery of EPs from a corpus of learners' recordings. We use Universal Phoneme Posteriorgram, derived from Multi-Layer Perceptron trained with a corpus of mixed languages, as features to bring supervised knowledge into the unsupervised task. We also use Hierarchical Agglomerative Clustering algorithm to explore sub-segmental variation of phoneme segments for distinguishing EPs. We tested K-means (assuming known number of EPs) and Gaussian Mixture Model with minimum description length principle (estimating unknown number of EPs) for EP discovery. Preliminary experimental results illustrated the effectiveness of the proposed framework, although there is still a long way to go compared to human annotators.