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

When evaluating the performance of automatic speech recognition models,usually word error rate within a certain dataset is used. Special care must betaken in understanding the dataset in order to report realistic performancenumbers. We argue that many performance numbers reported probably underestimatethe expected error rate. We conduct experiments controlling for selection bias,gender as well as overlap (between training and test data) in content, voices,and recording conditions. We find that content overlap has the biggest impact,but other factors like gender also play a role.

Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech