Pronunciation assessment is a major challenge in the computer-aided
pronunciation training system, especially at the word (phoneme)-level. To
obtain word (phoneme)-level scores, current methods usually rely on aligning
components to obtain acoustic features of each word (phoneme), which limits the
performance of assessment to the accuracy of alignments. Therefore, to address
this problem, we propose a simple yet effective method, namely
\underline{M}asked pre-training for \underline{P}ronunciation
\underline{A}ssessment (MPA). Specifically, by incorporating a mask-predict
strategy, our MPA supports end-to-end training without leveraging any aligning
components and can solve misalignment issues to a large extent during
prediction. Furthermore, we design two evaluation strategies to enable our
model to conduct assessments in both unsupervised and supervised settings.
Experimental results on SpeechOcean762 dataset demonstrate that MPA could
achieve better performance than previous methods, without any explicit
alignment. In spite of this, MPA still has some limitations, such as requiring
more inference time and reference text. They expect to be addressed in future
work.

本文提出了一种名为 MPA 的简单而有效的方法，通过采用掩码预测策略，支持端到端的训练过程，可在不使用任何对齐组件的情况下进行发音评估并在很大程度上解决误对齐问题。