  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.
