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.

本文提出了一种新颖的文本依赖模型来利用前置文本，通过注意力机制将音频与前置文本的音素序列对齐，来实现完全的端到端结构，同时提出了三种简单的数据增强方法来缓解模型捕捉错读音素的能力问题，并在 L2-ARCTIC 数据集上表现出 56.08% 的 F-measure 指标，相较 CNN-RNN-CTC 模型有明显提升。