  Automatic Pronunciation Assessment (APA) is vital for computer-assisted
language learning. Prior methods rely on annotated speech-text data to train
Automatic Speech Recognition (ASR) models or speech-score data to train
regression models. In this work, we propose a novel zero-shot APA method based
on the pre-trained acoustic model, HuBERT. Our method involves encoding speech
input and corrupting them via a masking module. We then employ the Transformer
encoder and apply k-means clustering to obtain token sequences. Finally, a
scoring module is designed to measure the number of wrongly recovered tokens.
Experimental results on speechocean762 demonstrate that the proposed method
achieves comparable performance to supervised regression baselines and
outperforms non-regression baselines in terms of Pearson Correlation
Coefficient (PCC). Additionally, we analyze how masking strategies affect the
performance of APA.
