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

Recent advances in automatic speech recognition (ASR) have achieved accuracylevels comparable to human transcribers, which led researchers to debate if themachine has reached human performance. Previous work focused on the Englishlanguage and modular hidden Markov model-deep neural network (HMM-DNN) systems.In this paper, we perform a comprehensive benchmarking for end-to-endtransformer ASR, modular HMM-DNN ASR, and human speech recognition (HSR) on theArabic language and its dialects. For the HSR, we evaluate linguist performanceand lay-native speaker performance on a new dataset collected as a part of thisstudy. For ASR the end-to-end work led to 12.5%, 27.5%, 33.8% WER; a newperformance milestone for the MGB2, MGB3, and MGB5 challenges respectively. Ourresults suggest that human performance in the Arabic language is stillconsiderably better than the machine with an absolute WER gap of 3.5% onaverage.

Arabic Speech Recognition by End-to-End, Modular Systems and Human