  Recently, end-to-end (E2E) models, which allow to take spectral vector
sequences of L2 (second-language) learners' utterances as input and produce the
corresponding phone-level sequences as output, have attracted much research
attention in developing mispronunciation detection (MD) systems. However, due
to the lack of sufficient labeled speech data of L2 speakers for model
estimation, E2E MD models are prone to overfitting in relation to conventional
ones that are built on DNN-HMM acoustic models. To alleviate this critical
issue, we in this paper propose two modeling strategies to enhance the
discrimination capability of E2E MD models, each of which can implicitly
leverage the phonetic and phonological traits encoded in a pretrained acoustic
model and contained within reference transcripts of the training data,
respectively. The first one is input augmentation, which aims to distill
knowledge about phonetic discrimination from a DNN-HMM acoustic model. The
second one is label augmentation, which manages to capture more phonological
patterns from the transcripts of training data. A series of empirical
experiments conducted on the L2-ARCTIC English dataset seem to confirm the
efficacy of our E2E MD model when compared to some top-of-the-line E2E MD
models and a classic pronunciation-scoring based method built on a DNN-HMM
acoustic model.
