EFFECTIVE GRAPH-BASED MODELING OF ARTICULATION TRAITS FOR MISPRONUNCIATION DETECTION AND DIAGNOSIS

Mispronunciation detection and diagnosis (MDD) manages to pinpoint phone-level erroneous pronunciation segmentations and provide instant and informative diagnostic feedback to L2 (second- language) learners. Among the various modeling paradigms for MDD, dictation-based neural methods have recently become a de facto standard, which identifies pronunciation errors and returns diagnostic feedback at the same time by aligning the recognized phone sequence uttered by an L2 learner to the corresponding canonical phone sequence of a given text prompt. Despite their decent efficacy, dictation-based methods have at least two downsides. First, the dictation process and alignment process are made independent of each other, often resulting in a poor diagnostic feedback. Second, prior knowledge about the articulation traits of the canonical phones in the text prompt is not fully utilized in MDD. On account of this, we propose a novel end-to-end MDD method that can streamline the dictation process and the alignment process in a non-autoregressive manner. In addition, knowledge about phone-level articulation traits are extracted with a graph convolutional network (GCN) to obtain more discriminative phonetic embeddings so as to promote the MDD performance. An extensive set of experiments conducted on the L2-ARCTIC benchmark dataset suggest the feasibility and effectiveness of our approach in relation to competitive baselines.