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

This paper describes our experiments with automatically identifying nativeaccents from speech samples of non-native English speakers using low levelaudio features, and n-gram features from manual transcriptions. Using apublicly available non-native speech corpus and simple audio featurerepresentations that do not perform word/phoneme recognition, we show that itis possible to achieve close to 90% classification accuracy for this task.While character n-grams perform similar to speech features, we show that speechfeatures are not affected by prompt variation, whereas ngrams are. Since theapproach followed can be easily adapted to any language provided we have enoughtraining data, we believe these results will provide useful insights for thedevelopment of accent recognition systems and for the study of accents in thecontext of language learning.

The Relevance of Text and Speech Features in Automatic Non-native English Accent Identification