  Traditionally, the performance of non-native mispronunciation verification
systems relied on effective phone-level labelling of non-native corpora. In
this study, a multi-view approach is proposed to incorporate discriminative
feature representations which requires less annotation for non-native
mispronunciation verification of Mandarin. Here, models are jointly learned to
embed acoustic sequence and multi-source information for speech attributes and
bottleneck features. Bidirectional LSTM embedding models with contrastive
losses are used to map acoustic sequences and multi-source information into
fixed-dimensional embeddings. The distance between acoustic embeddings is taken
as the similarity between phones. Accordingly, examples of mispronounced phones
are expected to have a small similarity score with their canonical
pronunciations. The approach shows improvement over GOP-based approach by
+11.23% and single-view approach by +1.47% in diagnostic accuracy for a
mispronunciation verification task.
