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/

Zendy Plus

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

Zendy Tools

Zendy Open

The attention-based Transformer model has achieved promising results forspeech recognition (SR) in the offline mode. However, in the streaming mode,the Transformer model usually incurs significant latency to maintain itsrecognition accuracy when applying a fixed-length look-ahead window in eachencoder layer. In this paper, we propose a novel low-latency streaming approachfor Transformer models, which consists of a scout network and a recognitionnetwork. The scout network detects the whole word boundary without seeing anyfuture frames, while the recognition network predicts the next subword byutilizing the information from all the frames before the predicted boundary.Our model achieves the best performance (2.7/6.4 WER) with only 639 ms latencyon the test-clean and test-other data sets of Librispeech.

Low Latency End-to-End Streaming Speech Recognition with a Scout Network