Mispronunciation detection and diagnosis (MDD) is a core component of
computer-assisted pronunciation training (CAPT). Most of the existing MDD
approaches focus on dealing with categorical errors (viz. one canonical phone
is substituted by another one, aside from those mispronunciations caused by
deletions or insertions). However, accurate detection and diagnosis of
non-categorial or distortion errors (viz. approximating L2 phones with L1
(first-language) phones, or erroneous pronunciations in between) still seems
out of reach. In view of this, we propose to conduct MDD with a novel end-
to-end automatic speech recognition (E2E-based ASR) approach. In particular, we
expand the original L2 phone set with their corresponding anti-phone set,
making the E2E-based MDD approach have a better capability to take in both
categorical and non-categorial mispronunciations, aiming to provide better
mispronunciation detection and diagnosis feedback. Furthermore, a novel
transfer-learning paradigm is devised to obtain the initial model estimate of
the E2E-based MDD system without resource to any phonological rules. Extensive
sets of experimental results on the L2-ARCTIC dataset show that our best system
can outperform the existing E2E baseline system and pronunciation scoring based
method (GOP) in terms of the F1-score, by 11.05% and 27.71%, respectively.

本论文提出了一种新的端到端自动语音识别方法，使用扩展后的原始 L2 语音集和其对应的反语音集，以更好地识别分类和非分类发音错误，并通过转移学习范式来获得 E2E-based MDD 系统的初始模型估计，在 L2-ARCTIC 数据集上进行了广泛的实验，结果表明我们的最佳系统在 F1 分数方面胜过现有的 E2E 基线系统和基于发音评分 (GOP) 的方法，分别提高了 11.05% 和 27.71%。