Automatic pronunciation error detection (APED) plays an important role in the
domain of language learning. As for the previous ASR-based APED methods, the
decoded results need to be aligned with the target text so that the errors can
be found out. However, since the decoding process and the alignment process are
independent, the prior knowledge about the target text is not fully utilized.
In this paper, we propose to use the target text as an extra condition for the
Transformer backbone to handle the APED task. The proposed method can output
the error states with consideration of the relationship between the input
speech and the target text in a fully end-to-end fashion.Meanwhile, as the
prior target text is used as a condition for the decoder input, the Transformer
works in a feed-forward manner instead of autoregressive in the inference
stage, which can significantly boost the speed in the actual deployment. We set
the ASR-based Transformer as the baseline APED model and conduct several
experiments on the L2-Arctic dataset. The results demonstrate that our approach
can obtain 8.4\% relative improvement on the $F_1$ score metric.

本文提出了一种使用目标文本作为条件的 Transformer 模型，以在完全端到端的方式下输出考虑输入语音与目标文本关系的错误状态，提高了速度，并在 L2-Arctic 数据集上实验表明相对于 ASR-based 模型，提高了 8.4％的 $ F_1 $ 分数指标。