  Recently, end-to-end mispronunciation detection and diagnosis (MD&D) systems
has become a popular alternative to greatly simplify the model-building process
of conventional hybrid DNN-HMM systems by representing complicated modules with
a single deep network architecture. In this paper, in order to utilize the
prior text in the end-to-end structure, we present a novel text-dependent model
which is difference with sed-mdd, the model achieves a fully end-to-end system
by aligning the audio with the phoneme sequences of the prior text inside the
model through the attention mechanism. Moreover, the prior text as input will
be a problem of imbalance between positive and negative samples in the phoneme
sequence. To alleviate this problem, we propose three simple data augmentation
methods, which effectively improve the ability of model to capture
mispronounced phonemes. We conduct experiments on L2-ARCTIC, and our best
performance improved from 49.29% to 56.08% in F-measure metric compared to the
CNN-RNN-CTC model.
