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

Non-autoregressive models greatly improve decoding speed over typicalsequence-to-sequence models, but suffer from degraded performance. Infillingand iterative refinement models make up some of this gap by editing the outputsof a non-autoregressive model, but are constrained in the edits that they canmake. We propose iterative realignment, where refinements occur over latentalignments rather than output sequence space. We demonstrate this in speechrecognition with Align-Refine, an end-to-end Transformer-based model whichrefines connectionist temporal classification (CTC) alignments to allowlength-changing insertions and deletions. Align-Refine outperforms Imputer andMask-CTC, matching an autoregressive baseline on WSJ at 1/14th the real-timefactor and attaining a LibriSpeech test-other WER of 9.0% without an LM. Ourmodel is strong even in one iteration with a shallower decoder.

Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment