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

The light gated recurrent units (Li-GRU) is well-known for achievingimpressive results in automatic speech recognition (ASR) tasks while beinglighter and faster to train than a standard gated recurrent units (GRU).However, the unbounded nature of its rectified linear unit on the candidaterecurrent gate induces an important gradient exploding phenomenon disruptingthe training process and preventing it from being applied to famous datasets.In this paper, we theoretically and empirically derive the necessary conditionsfor its stability as well as engineering mechanisms to speed up by a factor offive its training time, hence introducing a novel version of this architecturenamed SLi-GRU. Then, we evaluate its performance both on a toy taskillustrating its newly acquired capabilities and a set of three different ASRdatasets demonstrating lower word error rates compared to more complexrecurrent neural networks.

Stabilising and accelerating light gated recurrent units for automatic speech recognition