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

The increasing demand for learning English as a second language has led to agrowing interest in methods for automatically assessing spoken languageproficiency. Most approaches use hand-crafted features, but their efficacyrelies on their particular underlying assumptions and they risk discardingpotentially salient information about proficiency. Other approaches rely ontranscriptions produced by ASR systems which may not provide a faithfulrendition of a learner's utterance in specific scenarios (e.g., non-nativechildren's spontaneous speech). Furthermore, transcriptions do not yield anyinformation about relevant aspects such as intonation, rhythm or prosody. Inthis paper, we investigate the use of wav2vec 2.0 for assessing overall andindividual aspects of proficiency on two small datasets, one of which ispublicly available. We find that this approach significantly outperforms theBERT-based baseline system trained on ASR and manual transcriptions used forcomparison.

Proficiency assessment of L2 spoken English using wav2vec 2.0