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.

翻译：发音评估是计算机辅助发音训练系统中的一项重大挑战，尤其在单词（音素）级别上。为获得单词（音素）级别的评分，现有方法通常依赖对齐组件来获取每个单词（音素）的声学特征，这导致了评估性能受限于对齐精度。为解决这一问题，我们提出了一种简单而高效的方法，即掩码预训练发音评估（MPA）。具体而言，通过引入掩码预测策略，我们的MPA无需依赖任何对齐组件即可支持端到端训练，并在预测过程中极大程度地解决了对齐偏差问题。此外，我们设计了两种评估策略，使模型能够分别在无监督和监督设置下进行评分。在SpeechOcean762数据集上的实验结果表明，MPA无需显式对齐即可取得优于现有方法的性能。尽管如此，MPA仍存在一些局限，例如需要更长的推理时间和参考文本。这些问题有望在未来的工作中得到解决。