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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sequence-to-sequence models have shown success in end-to-end speechrecognition. However these models have only used shallow acoustic encodernetworks. In our work, we successively train very deep convolutional networksto add more expressive power and better generalization for end-to-end ASRmodels. We apply network-in-network principles, batch normalization, residualconnections and convolutional LSTMs to build very deep recurrent andconvolutional structures. Our models exploit the spectral structure in thefeature space and add computational depth without overfitting issues. Weexperiment with the WSJ ASR task and achieve 10.5\% word error rate without anydictionary or language using a 15 layer deep network.

Very Deep Convolutional Networks for End-to-End Speech Recognition