English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques