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

We show for the first time that learning powerful representations from speechaudio alone followed by fine-tuning on transcribed speech can outperform thebest semi-supervised methods while being conceptually simpler. wav2vec 2.0masks the speech input in the latent space and solves a contrastive taskdefined over a quantization of the latent representations which are jointlylearned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WERon the clean/other test sets. When lowering the amount of labeled data to onehour, wav2vec 2.0 outperforms the previous state of the art on the 100 hoursubset while using 100 times less labeled data. Using just ten minutes oflabeled data and pre-training on 53k hours of unlabeled data still achieves4.8/8.2 WER. This demonstrates the feasibility of speech recognition withlimited amounts of labeled data.

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations