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

Running automatic speech recognition (ASR) on edge devices is non-trivial dueto resource constraints, especially in scenarios that require supportingmultiple languages. We propose a new approach to enable multilingual speechrecognition on edge devices. This approach uses both language identificationand accent identification to select one of multiple monolingual ASR modelson-the-fly, each fine-tuned for a particular accent. Initial results for bothrecognition performance and resource usage are promising with our approachusing less than 1/12th of the memory consumed by other solutions.

Dyn-ASR: Compact, Multilingual Speech Recognition via Spoken Language and Accent Identification