# A multi-view approach for Mandarin non-native mispronunciation verification

arXiv: https://arxiv.org/abs/2009.02573 · HTML: https://mlsanity.com/paper/2009.02573/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Exploring Non-Autoregressive End-To-End Neural Modeling For English Mispronunciation Detection And Diagnosis](https://mlsanity.com/paper/2111.00844/) (2021)

## Abstract (arXiv)

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.