Attention Based Model for Segmental Pronunciation Error Detection

The Goodness of Pronunciation (GOP) algorithm is one well-established method of pronunciation assessment dependent on precise phoneme segment boundaries. The alignment process of canonical pronunciations to obtain these boundaries is prone to errors. To overcome this issue, the present paper proposes a combination of Bidirectional Long-Short Memory with saliency region selection with attention weights to provide estimates of mispronunciations without the need of phoneme boundaries. Three output and annotation configurations were used to train the model which was then assessed against a GOP baseline in the task of detecting segments with mispronunciations. The experiments were conducted using data from young Dutch learners of English, annotated by three trained phoneticians. The proposed model outperformed the GOP baseline. It was also found that the attention weights aligned themselves with the phoneme labels, helping detect mispronunciations and allowing further interpretation of the internal representation of the model.